{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies)=\n",
    "# Resampling strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampling strategies address class imbalance at the data level, by resampling the dataset to reduce the imbalance ratio. The resampling of an imbalanced dataset occurs before the training of the prediction model and can be seen as a data preprocessing step. Numerous methods have been proposed for resampling imbalanced datasets, which can be categorized into three main strategies: *oversampling*, *undersampling*, and *hybrid* strategies {cite}`fernandez2018learning,JMLR:v18:16-365,chawla2009data`. \n",
    "\n",
    "Oversampling consists in artificially increasing the proportion of samples from the minority class. The most naive approach is *random oversampling* (ROS), in which samples from the minority class are randomly duplicated {cite}`fernandez2018learning`. More sophisticated approaches consist in generating synthetic data by interpolating samples from the minority class. Two standard methods based on interpolation are *SMOTE* (Synthetic Minority Oversampling Technique) {cite}`chawla2002smote` and *ADASYN* (Adaptive Synthetic Sampling) {cite}`he2008adasyn`. \n",
    "\n",
    "Undersampling, on the contrary, consists in reducing the imbalance ratio by removing samples from the majority class. Samples may be simply randomly removed, as in *random undersampling* (RUS) {cite}`fernandez2018learning`. RUS is a fast and easy way to balance a dataset and is therefore widely used. A significant drawback of the method is that samples that are useful for the learning process may be discarded {cite}`ali2019review`. More advanced strategies aim at removing samples from overlapping regions (such as NearMiss {cite}`mani2003knn`, Tomek Links {cite}`tomek1976two` or Edited Nearest-Neighbors (ENN) {cite}`wilson1972asymptotic`), or by replacing subsets of samples by their centroids {cite}`yen2009cluster`.\n",
    "\n",
    "The ability of oversampling or undersampling techniques to improve classification performances largely depends on the characteristics of a dataset. As summarized in {cite}`haixiang2017learning`, oversampling techniques tend to be particularly effective when the number of samples from the minority class is very low. Undersampling techniques, on the other hand, are well-suited for large datasets. In particular, they allow to speed up the training times by reducing the dataset size. \n",
    "\n",
    "Oversampling and undersampling techniques can also be combined, resulting in *hybrid* resampling techniques. Hybridization of undersampling and oversampling has been shown to almost always increase the classification performances (Chapter 5, Section 6 in {cite}`fernandez2018learning`). Popular combinations involve SMOTE, together with nearest neighbors based undersampling techniques such as Tomek Links {cite}`tomek1976two,batista2004study` or Edited Nearest-Neighbors (ENN) {cite}`wilson1972asymptotic,batista2003balancing`.\n",
    "\n",
    "This section explores the use of some popular resampling techniques and discusses their benefits and limitations. The proposed implementation relies on the `imblearn` Python library, which is the most complete and up-to-date Python library for imbalanced learning. The library provides a wide range of resampling techniques that can be easily integrated with the `sklearn` library {cite}`Imblearn`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Initialization: Load shared functions and simulated data \n",
    "\n",
    "# Load shared functions\n",
    "!curl -O https://raw.githubusercontent.com/Fraud-Detection-Handbook/fraud-detection-handbook/main/Chapter_References/shared_functions.py\n",
    "%run shared_functions.py\n",
    "#%run ../Chapter_References/shared_functions.ipynb\n",
    "\n",
    "# Get simulated data from Github repository\n",
    "if not os.path.exists(\"simulated-data-transformed\"):\n",
    "    !git clone https://github.com/Fraud-Detection-Handbook/simulated-data-transformed\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustrative example\n",
    "\n",
    "For illustrative purposes, we reuse the same simple classification task as in the [cost-sensitive learning section](Imbalanced_Learning_Illustrative_Example). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "X, y = sklearn.datasets.make_classification(n_samples=5000, n_features=2, n_informative=2,\n",
    "                                            n_redundant=0, n_repeated=0, n_classes=2,\n",
    "                                            n_clusters_per_class=1,\n",
    "                                            weights=[0.95, 0.05],\n",
    "                                            class_sep=0.5, random_state=0)\n",
    "\n",
    "dataset_df = pd.DataFrame({'X1':X[:,0],'X2':X[:,1], 'Y':y})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "fig_distribution, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "groups = dataset_df.groupby('Y')\n",
    "for name, group in groups:\n",
    "    ax.scatter(group.X1, group.X2, edgecolors='k', label=name,alpha=1,marker='o')\n",
    "    \n",
    "ax.legend(loc='upper left', \n",
    "          bbox_to_anchor=(1.05, 1),\n",
    "          title=\"Class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 5000 samples with two classes, labeled 0 and 1. 95% of the samples are associated to the class 0, and 5% of the samples to the class 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the same methodology as in the [cost-sensitive learning section](Imbalanced_Learning_Illustrative_Example), the performances of a baseline classifier without resampling is obtained with the `kfold_cv_with_classifier` function. A decision tree of depth five and a 5-fold stratified cross-validation gives us the following baseline classification performances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_dt_baseline, classifier_0, train_df, test_df) = kfold_cv_with_classifier(classifier, \n",
    "                                                                                     X, y, \n",
    "                                                                                     n_splits=5,\n",
    "                                                                                     strategy_name=\"Decision tree - Baseline\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.008+/-0.002  0.008+/-0.005  0.906+/-0.025   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_dt_baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision boundary of the first classifier of the cross-validation is reported below. Due to class imbalance, the classifier tends to return equal probabilities for the two classes in the overlapping region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling\n",
    "\n",
    "Oversampling techniques aim at rebalancing the dataset by creating new samples for the minority class. The two most widely-used methods are *random oversampling* and *SMOTE* {cite}`fernandez2018learning,haixiang2017learning,chawla2002smote`. The next two subsections show how these methods can be implemented, and illustrate their ability to move the decision boundaries towards the minority class. \n",
    "\n",
    "#### Random oversampling\n",
    "\n",
    "Let us first briefly cover how the `imblearn` library allows to resample datasets. A more complete introduction can be found on the library's website, at https://imbalanced-learn.org/dev/introduction.html.\n",
    "\n",
    "The `imblearn` library provides objects called *samplers*, which take as input a dataset and a set of parameters that are specific to the sampler, and return a resampled dataset. \n",
    "\n",
    "For example, the `imblearn` sampler for random oversampling is called [`RandomOverSampler`](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.RandomOverSampler.html). Its main parameter is the `sampling_strategy`, which determines the desired imbalance ratio after random oversampling.\n",
    "\n",
    "Let us for example create a sampler for random oversampling, where the resampled dataset should have an imbalance ratio of 1 (that is, where samples from the minority class are duplicated until their number equals the number of samples in the majority class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_state is set to 0 for reproducibility\n",
    "ROS = imblearn.over_sampling.RandomOverSampler(sampling_strategy=1, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us apply this sampler on the `train_df` DataFrame, which is the set of training data in the first fold of the cross-validation performed above. The DataFrame contains 3784 samples of class 0 and 216 samples of class 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3784\n",
       "1     216\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resampling of the dataset is performed by calling the `fit` method of the sampler object. Let `train_df_ROS` be the resampled DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_resampled, Y_resampled = ROS.fit_resample(train_df[['X1','X2']], train_df['Y'])\n",
    "train_df_ROS = pd.DataFrame({'X1':X_resampled['X1'],'X2':X_resampled['X2'], 'Y':Y_resampled})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resampled DataFrame now contains as many samples of class 1 as of class 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3784\n",
       "1    3784\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_ROS['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Samplers can be combined with `sklearn` estimators using pipelines. The addition of a sampling step in the cross-validation procedure is therefore simple and consists in creating a pipeline made of a sampler, and an estimator. \n",
    "\n",
    "The implementation is provided below, in the `kfold_cv_with_sampler_and_classifier` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def kfold_cv_with_sampler_and_classifier(classifier,\n",
    "                                         sampler_list,\n",
    "                                         X,\n",
    "                                         y,\n",
    "                                         n_splits=5,\n",
    "                                         strategy_name=\"Baseline classifier\"):\n",
    "    \n",
    "    # Create a pipeline with the list of samplers, and the estimator\n",
    "    estimators = sampler_list.copy()\n",
    "    estimators.extend([('clf', classifier)])\n",
    "    \n",
    "    pipe = imblearn.pipeline.Pipeline(estimators)\n",
    "    \n",
    "    cv = sklearn.model_selection.StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=0)\n",
    "    \n",
    "    cv_results_ = sklearn.model_selection.cross_validate(pipe,X,y,cv=cv,\n",
    "                                                         scoring=['roc_auc',\n",
    "                                                                  'average_precision',\n",
    "                                                                  'balanced_accuracy'],\n",
    "                                                         return_estimator=True)\n",
    "    \n",
    "    results = round(pd.DataFrame(cv_results_),3)\n",
    "    results_mean = list(results.mean().values)\n",
    "    results_std = list(results.std().values)\n",
    "    results_df = pd.DataFrame([[str(round(results_mean[i],3))+'+/-'+\n",
    "                                str(round(results_std[i],3)) for i in range(len(results))]],\n",
    "                              columns=['Fit time (s)','Score time (s)',\n",
    "                                       'AUC ROC','Average Precision','Balanced accuracy'])\n",
    "    results_df.rename(index={0:strategy_name}, inplace=True)\n",
    "    \n",
    "    classifier_0 = cv_results_['estimator'][0]\n",
    "    \n",
    "    (train_index, test_index) = next(cv.split(X, y))\n",
    "    X_resampled, Y_resampled = X[train_index,:], y[train_index]\n",
    "    for i in range(len(sampler_list)):\n",
    "        X_resampled, Y_resampled = sampler_list[i][1].fit_resample(X_resampled, Y_resampled)\n",
    "    \n",
    "    test_df = pd.DataFrame({'X1':X[test_index,0],'X2':X[test_index,1], 'Y':y[test_index]})\n",
    "    train_df = pd.DataFrame({'X1':X_resampled[:,0],'X2':X_resampled[:,1], 'Y':Y_resampled})\n",
    "    \n",
    "    return (results_df, classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us assess the performances of the baseline classifier combined with random oversampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "sampler_list = [('sampler',imblearn.over_sampling.RandomOverSampler(sampling_strategy=1, random_state=0))]\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_ROS, classifier_0, train_df, test_df) = kfold_cv_with_sampler_and_classifier(classifier, \n",
    "                                                                                         sampler_list, \n",
    "                                                                                         X, y, \n",
    "                                                                                         n_splits=5,\n",
    "                                                                                         strategy_name=\"Decision tree - ROS\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, we can verify that the training set size contains the same number of samples for the two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3784\n",
       "1    3784\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resampling allowed to shift the decision boundary towards the minority class, as can be seen in the figure below. Note that the training data for the minority class looks the same as the baseline classifier. Most instances have however been duplicated many times to reach an imbalance ratio of one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classification performances show an increase in terms of balanced accuracy. We however note a decrease in terms of AUC ROC and Average Precision, due to the shift of the decision boundary which significantly increased the number of false positives. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ROS</th>\n",
       "      <td>0.019+/-0.01</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.88+/-0.038</td>\n",
       "      <td>0.456+/-0.062</td>\n",
       "      <td>0.888+/-0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.008+/-0.002  0.008+/-0.005  0.906+/-0.025   \n",
       "Decision tree - ROS        0.019+/-0.01  0.008+/-0.003   0.88+/-0.038   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  \n",
       "Decision tree - ROS          0.456+/-0.062      0.888+/-0.03  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_ROS])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SMOTE\n",
    "\n",
    "SMOTE {cite}`chawla2002smote` oversamples the minority class by generating synthetic examples in the neighborhood of observed ones. The idea is to form new minority examples by interpolating between samples of the same class. This has the effect of creating clusters around each minority observation. By creating synthetic observations, the classifier builds larger decision regions that contain nearby instances from the minority class. SMOTE has been shown to improve the performances of a base classifier in many applications {cite}`dal2015adaptive,chawla2002smote`\n",
    "\n",
    "The `imblearn` sampler for SMOTE is [`imblearn.over_sampling.SMOTE`](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html). Let us illustrate the use of SMOTE, and its impact on the classifier decision boundary and the classification performances.\n",
    "\n",
    "The implementation follows the same structure as the random oversampling. The only difference is to change the sampler to SMOTE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "sampler_list = [('sampler', imblearn.over_sampling.SMOTE(sampling_strategy=1, random_state=0))]\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_SMOTE, classifier_0, train_df, test_df) = kfold_cv_with_sampler_and_classifier(classifier, \n",
    "                                                                                           sampler_list, \n",
    "                                                                                           X, y, \n",
    "                                                                                           n_splits=5,\n",
    "                                                                                           strategy_name=\"Decision tree - SMOTE\")\n",
    "\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with random oversampling, the number of samples in the minority class is increased to match the number of samples in the majority class (`sampling_strategy=1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3784\n",
       "1    3784\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision boundary is also shifted towards the minority class. We note that, contrary to random oversampling, new examples have been generated for the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ffMG3335LSEgIP/74I7dv36ZevXom29WsWZN27doxdepULly4wIULF4xVF59//vlsn9/169eZPXs2fn5+XL9+nR9++IG6desC4O7ujq+vL76+vvj7+zNnzpwMn6qmx8nJiatXr3L+/HkCAwOZP38+Fy9eNAaSoUOHcvDgQXbs2MHNmzdZtGgREREReHh4MGzYMA4fPsyGDRu4efMmW7duZdWqVSbDlpLZ2dkxYMAA5s6dy5kzZ7hy5QpTpkyhQoUKaa5zRuzs7PD39ycyMjLTbV9//XU2bdrEnj17CAoK4rPPPmPXrl3p/uKoVasWLVq0YMaMGVy+fJmjR4+ydetW4/s9evQgLi6OmTNnEhAQwM8//8zs2bMpVapUptcopcjISObOnYuvry8hISHs2rULOzs7qlevbrJdt27dSExM5P333ycgIIBjx47h7e3NgAED0n2Sm5XrduvWLe7cuZPu+6+++iqXLl1i8eLFBAYGcvjwYRYuXEiFChWyfS1FWhKfJD6lJvHJ9LpJfDIPiU0Sm1KT2GR63SQ2FRMFVJTLLNq1a6dcXV2N/7Vs2VKNGDFC/fbbbybbhYeHq7fffls1atRINW7cWI0bN07dvXvX+P6ff/6p/vOf/6h69eqpzp07q/379yullEnlQ6WU2rdvn3r55ZdVvXr1VPv27dXmzZuVUipN5cMHDx6ot99+W3l4eKgmTZqoqVOnqocPH6a7T6XSr2CYLCwsTI0fP141bdpUNWjQQI0aNUqFhoYqpZSKjY1V06ZNUw0bNlSenp5q+/btqmPHjiaVD1NXhkz5WlRUlBo/frzy8PBQzZs3V2PHjlUrVqxQHTt2NG6/e/du1aFDB+Xu7q769++vLl68aHzvwIEDqkuXLqpu3bqqc+fOas+ePRn+rPR6vfroo49Us2bNVMOGDdWbb76pbt26ZXx/8ODBasmSJRl+ftmyZcrd3V3NmzfPpFJgRp/fuHGjat++vapXr57q0aOHOnHiRIb7vn//vnrrrbdUgwYNVNeuXdWKFSuMlQ+VUurvv/9WgwcPVu7u7qpVq1ZqwYIFJj+/jK5RysqHCQkJauHChcrT01PVq1dP9erVS509e1YppdKcz6VLl9SgQYNUvXr1VJs2bdTnn3+uEhIS0uwzWUYVQJVS6vz586pVq1aqWbNmKjEx0aQyZrJffvlF9e7dW9WtW1e1bdtWrVu3zuT97FxL8YTEJ4lPGX1e4lMSiU/mIbFJYlNGn5fYlERiU/FhoZSsCi2EEEIIIYQQouQo9kOjhRBCCCGEEEKIlCQRFkIIIYQQQghRokgiLIQQQgghhBCiRJFEWAghhBBCCCFEiSKJcDHRtWvXNOvzffPNN7i5ubFhwwaT15cuXUrPnj0BcHNz45dffgEgPDzcuI5e6vey4vDhw9y7dy9H7Q8JCcHNzY2bN29muu3Zs2dxc3NLd5H7vGYwGHBzc+Ps2bNZ2v7SpUv8/vvv+dwqIYQQQhS0on6vlVJUVBS7d+/O8vY7duygffv2WdpWKcXWrVtN1pEWojCSRLiYaNKkCRcuXDB57cyZM5QvX54zZ86YvP7nn3/SrFkzAHx9fWnSpAkAixYt4scff8zR8UNDQ5kwYQLR0dE5+nylSpXw9fXFxcUl0209PDzw9fVFq9Xm6Fj56a233uLGjRvmboYQQggh8lhRv9dK6auvvmLHjh253k96fvvtNz788ENJhEWhJ4lwMdG0aVMuXrxo/LtSil9//ZURI0bw+++/k5CQAEBiYiIXL140Budy5coZF4rPzUpauV2FS6PRUK5cOTQaTabbWltbU65cuVwdTwghhBAiO4r6vVZ+7asg9y1EXpJEuJho2rQpERERBAYGAnDlyhXi4uIYMGAABoOBv//+G4CrV6+i1+tp2rQp8GRIjre3N3v27MHHx8dk6Msff/xBjx49cHd359VXXyU4ODjd43fo0AGAzp07s3v3bry9vRk9ejRDhgyhadOmnDx5krt37zJ+/HiaNm1KvXr16NmzJ7/99huQdmi0m5sbe/fupXv37nh4eDBkyBCCgoIA06HRyZ/7/vvv6dSpE40bN2b06NHcv3/f2DZfX1+6d+9O/fr1GTlyJB9//DHTpk3L8Fp+9tlntGzZkhYtWrBnzx6T9552DkOGDCE0NJSZM2ca93/8+HF69eqFu7s7jRs3ZuLEiTx+/DgLP1EhhBBCFCaF7V4L4OjRo3h5edGgQQN69erFyZMnjdtfuXKFQYMG0bBhQ1q1asX8+fMxGAzs3r2bzz77jD/++AM3N7d0j3Xnzh1GjhxJw4YN6d27NyEhISbvZ3R/ExISwtChQwGoW7cuZ8+eJT4+nk8//ZQ2bdpQt25d2rVrxzfffJPdyy9EnpNEuJioWLEiLi4unD9/HkgaqtOkSRN0Oh0eHh7GITt//vknbm5ulCpVyuTzw4cPp0uXLrz00kvs3LnT+Pr27duZPn06O3fuJDIykoULF6Z7/OThNdu2baNr165AUpB86aWX2LRpE40aNWLKlCkYDAa+/fZb9u7dS8WKFfnggw8yPKfPPvuMGTNmsHHjRsLCwliyZEmG265evZpFixaxatUqLly4wPr16wEIDg5mzJgxvPTSS+zduxd3d3e2bNmS4X62bdvGxo0bmTdvHl999RW7du0yef9p5+Dt7U3FihWZNm0a7733HsHBwYwbN44BAwZw6NAhli9fzpkzZ9i6dWuGxxdCCCFE4VTY7rUuX77Mu+++yxtvvIGPjw/9+vVj7NixXLp0CYB3332XmjVr4uPjw7Jly9i3bx87d+6ka9euDB8+nPr16+Pr65vusSZMmEBiYiI7duxg5MiRbNy40fje0+5vKlWqhLe3NwAnT57Ew8ODtWvX8uOPP7JixQoOHz5Mr169mDNnDnfu3MnJj0GIPCOJcDGScsjO2bNnad68OQDNmzc3Bufz588bh+qkZG9vj06nw9ramjJlyhhfHzVqFC1btsTNzY2+ffty+fLldI+d/JnSpUuj0+kAcHJyYvDgwTz33HM4ODjQrl07Zs2aRa1atfi///s/Bg0aREBAQIZDaF577TVatmyJu7s7AwcONBmOlNrYsWNp0KABTZs2pXv37sZtd+zYQd26dRk7diw1a9ZkwoQJNGzYMMP9bN++nSFDhtCuXTuef/55Zs+ebfL+087ByckJjUaDg4MDjo6OJCQk8N5779G/f39cXFzw9PTkhRde4Nq1axkeXwghhBCFV2G611q/fj19+vShZ8+eVK1alYEDB+Ll5cWmTZuApDnFpUuX5tlnn6Vp06asXbsWT09PdDoddnZ2aLXadKea+fv7c+7cOWbPnk3t2rXp2rUrAwYMML7/tPsbjUZjfABQtmxZrK2tcXV1Ze7cuTRs2JAqVaowevRoEhISpKaKMLvCV21I5Fjjxo3ZsWMHiYmJ/P7777z11lsANGvWjNWrV5OQkMC5c+d45513srzPqlWrGv/s6OhIbGxslj9buXJlk78PHDiQgwcP8scff3Djxg3++usvAOOcmqcd28HB4alVojPa9sqVK9SrV89k2wYNGhAREZHufgICAhg9erTx766urtjY2GTpHFIX76pevTrW1tasXLkSf39//P39uXbtGl5eXhmehxBCCCEKr8J0rxUQEMDVq1dNRq/Fx8dTv359AN5++23mzJnDtm3baNOmDV5eXmnuidJz7do1HBwcTAqY1qtXj++//x7I/v1Nx44d+fnnn5k/fz7Xr1/Hz88PQIppCbOTHuFipGnTply6dMlY0bBOnToAuLu7A0lV/IKCgoxzVrIidfGq7BRASJlAJiYmMnz4cNavX0+lSpUYMWIECxYseOrnrayssnzsjLZNr/hWZueQ+v3kfWT3HC5fvoyXlxf+/v40btyYuXPnGoeNCyGEEKLoKUz3WgkJCYwYMYK9e/ca/ztw4ACLFy8GYNCgQRw7doyxY8dy//593nzzTeOw5cykbkPKh/3Zvb9ZunQpkydPRqPR8Morr7Bt27YstUGI/CY9wsVI9erVKVWqFDt37qRJkyZYWiY957CysqJRo0bs2LEDV1fXNHNWkllYWOT42Jl99tq1a/z222+cOnWK8uXLAxjn6uZndcHatWunWQP477//pkqVKhluf/HiRTp37gzAzZs3jcsUZPcc9u3bR6NGjUzmNt+8eZNq1arl/sSEEEIIUeAK071WjRo1CA4ONrmvWLFiBU5OTvTv35+FCxcyYsQIhgwZwpAhQ/jiiy/w8fFh3LhxT22Hq6srUVFRXL9+nZo1awIYe3Eh8/ub1Pv+9ttvmTVrFt26dQMwThGT6tLC3KRHuJhp0qQJBw4cMM5ZSdasWTOOHTuW7pyVZHZ2dty6dStHxQvs7OyApKeEUVFRad5/5plnsLS05ODBg4SGhnL48GHjU8m4uLhsHy+r+vXrx19//cWqVau4ceMGq1ev5vfff8/wF8CgQYPYvHkzhw4d4urVq8ycOdP4Sy4r52Bvb8/169d5+PAhTk5OXL16lfPnzxMYGMj8+fO5ePEi8fHx+Xa+QgghhMhfheVea9iwYRw+fJgNGzZw8+ZNtm7dyqpVq6hatSo2Njb88ccffPzxxwQEBHDlyhVOnjxJ3bp1jfu6d+9euhWqa9WqRYsWLZgxYwaXL1/m6NGjJoU+M7u/SW6nn58fsbGxODk5cfz4cYKDg/nf//7HlClTgPy9/xMiKyQRLmaaNm1KdHR0muDcvHlz9Hr9U4PzK6+8QlBQED169Mj2U7rSpUvTu3dvJk+ebFIJMVnFihX58MMP+eqrr/Dy8mL16tXMnDkTKysrY3XD/FC5cmVWrFjBnj176N69O3/88QcdO3ZMM5Q6Wc+ePZkwYQJz585l0KBBtG3bFnt7+yyfw6BBg4xPPocMGUKjRo14/fXXGTBgAKGhoSbVHIUQQghR9BSWe62GDRuyaNEitm/fjpeXFxs2bGDevHm0bdsWSBqSHBsbS79+/Xj11VdxcXFh1qxZQNISTJaWlnTr1o3w8PA0x1q2bBnOzs4MGDCApUuXMmTIEON7md3fuLq64unpyauvvsrJkyeZN28eV69excvLi6lTp/Lyyy/TsGFDk15mIczBQsm4BFGMXb16FYPBYJzDA/Df//4Xd3d3xo0bZ8aWCSGEEEIIIcxFeoRFsRYUFMSwYcP4+eefCQ0NZceOHZw+fZpOnTqZu2lCCCGEEEIIM5EeYVHsrVy5km3bthEeHk6NGjUYP348HTt2NHezhBBCCCGEEGYiibAQQgghhBBCiBJFhkYLIYQQQgghhChRCkUiHB4ezosvvkhAQIC5myKEECYkPgkhhBBCFD9aczcgPj6e999/H51Ol6XtY+MM6GOyvw6rpaUliYmJ2f5cQZN25q38aqcB0Flr0FnGQkwsCXoDysomR/vSaCxJSCj81zI/26krUzpf9ptb2YlPOY1NIN+nvFQU2gj5H5tstIlY6iMxRCfkODaBxKfCGpuyKzfxqSAVle9vatLujBkAe50F1sShYuJydb8ERScmpZYf7S4u8cmczJ4If/rppwwYMIA1a9ZkaXt9TDy+/7ue7eM4OjoSGRmZ7c8VNGln3sqvdt43QO0apXGzuYqlfyAP/75HQsWaOdrXM8848uhR4b+W+dnOWv/pky/7za3sxKecxiaQ71NeKgpthPyPTTXK6LH/6yjh5x/lODaBxKfCGpuyKzfxqSAVle9vatLujN03QLO6VlROCCbBPzhX90tQdGJSavnR7uISn8zJrInw7t27KVOmDK1bt85yImxpaYmjo2O2j6XRaHL0uYIm7cxb+dVOfYwBW1tbbLTWaG1tibWzxfKZnB1HY6nhmRx+tiAVlXbmlezGp5zGJpDvU14qCm2E/I9NtrZgY22DbS5iExSd731RaacQQojCw6yJ8K5du7CwsOD06dNcunSJqVOnsnLlSsqVK5fhZxITE3P09KqoPK2Tduat/Gqn3gB6vY5Ymzji9Xr00XoScvikr6g83czPdmb8jTef7MannMYmkO9TXioKbYT8j016vR7LuNhcxSaQ+FQYY5MQQoi8YdZEeMuWLcY/DxkyhA8//PCpSbAQQhQUiU9CCCGEEMWX2ecICyGEEEIIIYQoWPHx8f0F3EQAACAASURBVAQFBRETE2PupmRIp9NRtWpVrKys8nzfhSYR3rRpk7mbIIQQ6ZL4JIQQQojiJigoCI1GQ/ny5bGwsDB3c9JQShEVFUVQUBC1atXK8/0XinWEhRBCCCGEEEIUnJiYGOzt7QtlEgxgYWGBvb19vvVYSyIshBBCCCGEECVQYU2Ck+Vn+wrN0GghhBBCCCGEEIXL9evX+eKLL4iJiUGv19OyZUs8PDzYt28fs2fPNnfzckwSYSGEEEIIIYQQaURGRvLBBx8wb948qlSpQkJCAjNnzqRs2bLmblquSSIshBBCCCGEECKNU6dO0bhxY6pUqQKARqNh1qxZXLx4kXPnzgGwc+dOfvrpJwwGAw4ODsybN4/bt28zd+5ctFqt8TNarZb3338fpRQGg4F33303X4pgZZUkwkIIIYQQQggh0ggLC+PZZ581ec3Ozs64nFFiYiKPHj1i+fLlWFpaMmnSJC5duoS/vz9ubm6MHz+e8+fPExkZyT///IODgwMffvghN27cICoqyhynZCSJsBBCCCGEEEKINCpWrMjVq1dNXrt16xZ//vknAJaWlmi1Wj744APs7Oy4e/cuBoOBbt26sWXLFt5++23s7e0ZPXo0LVq0IDg4mKlTp6LVahk2bJgZzugJqRothBBCCCGEECKNVq1acebMGUJCQgAwGAx4e3vj5OQEwLVr1zh16hQff/wxkyZNQimFUopTp07RoEEDVqxYQfv27dm8eTPnzp2jbNmyLFu2jGHDhrFq1Spznpr0CAshhBBCCCGESMve3p6ZM2fy6aefopQiOjqaVq1aUa1aNf78809cXFzQ6XQMHz4ca2trypYtS1hYGHXr1mX27NloNBosLS0ZP348FStW5P3332f79u1oNBqz9whLIiyEEEIIIYQQIl3PPfcc3t7eaV5v3LgxQLrvAaxZsybNa8uXL8/bxuWCDI0WQgghhBBCCFGiSCIshBBCCCGEEKJEkURYCCGEEEIIIUSJIomwEEIIIYQQQogSRRJhIYQQQgghhBAliiTCQgghhBBCCCFKFFk+SQghhBBCCCFEgUpMTGTRokVcu3YNa2trpk2bhouLS4EdX3qEhRBCCCGEEEI81c5du2jU/AXKV6hAo+YvsHPXrlzt7+TJk8TFxbFmzRpGjx6d4XrE+UV6hIUQQgghhBBCZGjnrl28O/Mj7DuOo0rvOsSG+PHuzI8A6NunT472eeHCBVq0aAFAvXr1uHz5cp61NyukR1gIIYQQQgghRIbmLViMfcdx6KrVx0KjRVetPvYdxzFvweIc7zMqKgp7e3vj3zUaDQaDIS+amyWSCAshhBBCCCGEyFBIYAA2LnVMXrNxqUNIYECO92lvb090dLTx74mJiWi1BTdgWRJhIYQQQgghhBAZcqlei9gQP5PXYkP8cKleK8f7dHd35/Tp0wD89ddf1KqV833lhCTCQgghhBBFREJCAtOnT2fAgAEMGjSIoKAgczdJCFECzJgymaij3sTcvIBKMBBz8wJRR72ZMWVyjvf54osvYm1tzahRo1ixYgXjx4/PwxZnToplCSGEEEIUEcePHwfg22+/5ezZs3zyySesXLnSzK0SQhR3yQWx5i1YTPD2AFyq1+LDOR/kuFAWgKWlJVOmTMmrJmabJMJCCCGEEEVEx44dadu2LQC3bt3C2dnZvA0SQpQYffv0yVXiW9hIIiyEEEIIUYRotVqmTp3KkSNHWLFiRabbW1pa4ujoWAAtyx2NRlMk2pmatDtj+hgD1taW2GJHgq0tsXa2WD6T82NqLDU8k4vPm0tRbXdxJ4mwEEIIIUQR8+mnn/LOO+/Qr18/Dhw4gJ2dXYbbJiYmEhkZWYCtyxlHR8ci0c7UpN0Z0xsgLs4KfUI0CXo9+mg9CY9yfsxnnnHkUS4+by750e5yebq3kkkSYSGKMR/fk6zauRX/22HUruTM6L4D6e7ZxtzNEkIIkUN79+7lzp07jBo1CltbWywsLNBoNOZulhBCFDmSCAtRTH20fi0+P/3Ao1jF886W9KwVwfLNawAkGRZCiCKqc+fOTJ8+nUGDBmEwGJgxYwY2NjbmbpYQQhQ5Zk+EExISmDlzJjdu3ECj0fDJJ59QtWpVczdLiCLNx/ckR37+gd39bPGsqsE3KIFhe/UMaWDFqp1bJRHOAolNQojCyM7OjuXLl5u7GUIIUeSZfR3hlMsAjB8/nk8++cTMLRKi6FuyZQPf9LalXQ0tVhoL2tXQsqGnLVsuxON/O8zczSsSJDYJIYQQQuS/v//+m7Fjxxb4cc2eCHfs2JGPP/4YkGUAhMgrIeGP8KxqOmfMs6qGmxGK2pXkO5YVEpuEEEIIIZ7YvWsnbZo3okKF8rRp3ojdu3bmep9btmxh/vz5xMbG5kELs8fsQ6Mhe8sA5HQJgKJS2l7ambfyq536GAO2trbYaK3R5nI5gPwoqW9vDb5BCbSr8eQr7huUgL01TBoyPEfHK4ml/wsiNoF8n/JSUWgj5H9ssrUFG2sbbEvIUiVFpZ1CCFFU7d61k09mTebLLuD5qgO+QeEMnzUZgN59+uZ4v88++yzz5s1j9uzZedXULCsUiTBkfRmAnC4BUFRK2xe3dp44doDNX6/mbmgg5StXZ/Bro2jbwasAWpgkv66n3gB6vY5Ymzjic7kcQH6U1DckwLB9eja8kmKO8D49hgTo2Lhpjo6Xn0sWFOYlAPI7NkHx+96bU1FoI+R/bNLr9VjGxZaYpUryq52FOTYJIURBWrZgHl92wdjJ0q6Gli+7GHhrwbxcJcLt2rXj9u3bedXMbDF7IizLABRfJ44dYOXKZTh2HkcVlzrEhvixcuUygAJNhksaH9+TaCwtiI5TvOGj58ZDRQ2npL+XKQI9ZYWFxCYhhBAiLXN3cgjzuBIYguerDiaveVbVcCUwxEwtyj2zzxHu3Lkzfn5+DBo0iBEjRsgyAMXI5q9X49h5HLpq9bHQaNFVq49j53Fs/nq1uZtWbPn4nmTJxpVMbGGFpYXpexaWVrw79HXzNKwIktgkhBCisDpx7AAjh/agR4f6jBzagxPHDhTYcVeuXAaeI6kyeTd4jmTlymUFdnxhPm7VXfANSjB5zTcoAbfqLmZqUe6ZvUdYlgEovu6GBlLFpY7JazYudQgODTRPg0qAJVs28HUPK9rV0FK3nIa5p2IBRbjegg/+O0aWTcoGiU1CCCEKI3OOuEvZyQEk/f/fTg7pFS7eJk6ZwfBZk/myi8E47W74IZj+8QxzNy3HzJ4Ii+KrfOXqxIb4GYMlQGyIH+UrVzdfo4opH9+TrNq59d9q0UnDnwe6WzHQ3Yr4BIXNnEhJgoUQQohiwJzJqHRylFzJ84DfWjCPK4EhuFV3YfrHM3I1PzhZpUqVWLt2ba73k11mHxotiq/Br40i8gdvYm5eQCUYiLl5gcgfvBn82ihzN61Y8fE9yfLNa1jZMYpqpSzSHbZib22mxgkhhBAiT90NDcQmnWT07lOS0bwaSp3cyZFScenk8PE9idfEMbj2/w9eE8fg43vS3E0qdHr36cvJs39w585dTp79I0+SYHOSHmGRb5KfSm7+ejXB/xZUGDNmYqEbOlPUiz6s2rmVL7tZ0q6Glkexit7bo3kUC887W9LzOS2bLsSjlEXmOxJCCCFEoZfdEXcZDaXW6XS0aNUhW8ce/NqopGHYncdh8+++In/wZsyYibk5JbNL7lT4spslnlUd8A2KYvjmNQAyoq4Yk0RY5Ku2HbwKdVJZHCpb+98Ow7OqA1svxmOlsWBrnydLJg3arafnc1pW/y/e3M0UQgghRB7IbjKa0VDq9Wu9s50IF5VOjuxK2akA/y4N1M3AmJ1bJREuxiQRFgWmMPa8FoeiD7UrOeMbFMXcU7Fs7WNrEsS39LblDR89tSvJaphCCCFEcZBeMtq2dVs2f72aJfOmp7nHynBeb/D1HB+/qNwjZVVyp0JKnlU1+N8OM1OLCo5SCguLwjtyUCmVb/uWRFgUiKz2vOY2WU79+ZGjJjz1aWdxKPowuu9AXtu4ktCIRDyrmq5z61lVw/UHiiVDBpqpdUIIIYTIaymT0czusTIaSl2hSs1Mj3Pi2AHWrV5GRNg/WFjpcHR05I0xk4tdIpzcqZDcmQBJNVZqV3I2Y6vyn06nIyoqCnt7+0KZDCuliIqKQqfT5cv+JREWBSIrPa+5Haac3ueXLpvPmJiYDD9fVCtbJ1eJ9r8dRuUyjkToE3nGJilopw7iLmWfkWE9QgghRDGV2T1WRkOpJ02a9tT9njh2gM8+W4RTl0mU+vdzYQeX4r3sE6DoTCHLitF9BzJ88xq+7JZiaaDvEpkwuHh3JFStWpWgoCDu3r1r7qZkSKfTUbVq1XzZtyTCokBkpec1t8OU0/18p6d/vigWfUhb0MHAsH2J9HC1YsR+Pet72JoE8bcHDzN3k4UQQgiRTzK7x8poXm+nl3sSGRmZ4X43f70apy6TTO6rnLtOIvywd5GaQpYVyR0GY/7tZKhdyZkJgwcW+44EKysratWqZe5mmI0kwsIoP+fwZqXnNbfDlHPy+aJY9CG9gg5D6lvxxW9xRMRCn+3RPIwBe2v4ePT4Yh/EhRBCiJIsK/dYOZnXm9F9lSHiDncjctXkQqm7Zxu5ZyphZB1hATwZVoznSKpM3g2eI1m5clmO15lLLbM1hU8cO4BGZ5+rtelSrm0X5fcTt9a/SdDiXmBlw6C+7fPsXMwtqaCDhq0X46n3xWMsP3rEl+fi2dXPjpj3HNnVz44qpSx4xs5RAroQQghRzGV2j5VTGa0ZrC1VodBPIRMiK6RHWAD5Xz35aT2vyUm4XcOuhB1ajnOXCTkappw8zFnv1oYovxMm+8loTktRXD6pdiVnPvopgm8uxrO+hy3jDsXg3UVn0kO84RVbXjtQ+IoeCCGEECJv5dfotsGvjTLOEU55P6VJiGfwa4V3CpkQWSWJsAAKpnpyRsNyUibh1s5VuX90FfHhwWh1Dkx4e2aWA3nydsuXzMH5lemZzmk5cewAy5fMoWyqbQv78kmj+w7kw9Xe7OmftFTSpbD0q0WH3s943o8QQgghio/8WNIoeX/rVi/jTsqq0eOmF9p7JCGyQxJhAeR/9eSnzT9OmYTb13kR+zovohIMBC/une1A27aDF0vmTccmkzktyT3BhpjH6W5b2JdPehSrjMnv886W6VaLLu4l/4UQQgiRv4rLmsEpV9uoXcmZ0X2LfyEskTmZIyyA/JtfApnPP85oDkpOk/CszGnZ/PVqtG5tsLC2y9Nj5xcf35N4TRyDa///MHvt58alkgDea23DiP16jt8wEJ+gOH7DwPDvEhndt3iX/BdCCCGKmxPHDjByaA96dKjPyKE9ik19E3NKXm1jZccoYt5zYGXHKJZvXoOP70lzN02YmfQICyB/qyfnZH27h4eWYqOxoEeH+lmuYJ3c63wn5AYWu+eg4vVYlamCrWtLov7+0WROy93QQLR6A46NuhF+aDllU8wnvuezgJc7dsn1eeeV1MslffRTLN6/JtBhYzQ1S1vwwYs2vOpuRe/t0UTEgOuz5UpEyX8hhBCiOCmKdUuKgvRW2/iym4ExO7fKvVIJJ4mwMMrr4S8pE9Oq2Vjf7pmyFVCGeGy7TMEpi78Ikn95aN3aoImMwrnLBAyRYUT8vJVHp7djYaVDo9WyZN501q5cjKWNLfHhwXAVbGs1/XdecghWZVxIjIrgxKkTPF+vYaH4xZMygG+9GM83F+PZ29/OuFbwwF167kYpnnW0wE7nwIFlK83dZCGEEEJkU34XLi2pklbbcDB5zbOqBv/bYWZqkSgsZGi0yBcph0Nbla2S6fDjth28WLdxP/uPXUCns6V09ynoqtXHQqNFV60+Wrc2LF8yhx4d6jO4XycG9W1vMmwo+ZeH3v80zl0mkBD1gAjfLZR9eRxV39lD+T6zSLDSYd/gZaIMCuee71H1nT2U6TQafcBvlGrZn2pT9lOm02isnKvg+O8vnsLg6q17jDsUg2b2I946qGd9j6QiWVYai6TkuI8tdcpZsqmXLRpiZKiPEEIIUQTdDQ1Mt27J3SzWLZFh1emrXcnZOJ0smdRSESCJsMgnKZ9qlnqhP+GHlj91DeGUgftOyA2TXwRRfj8R5XeCsq9Mp8rk3eg6jiPKoCjTdaJxvvGd0KTPxIeHYONSh4jT2yjbZYJJMu3cdRLRl0/i3HWSyetlu0wg4pdtxNy8QPih5ZRq2T9bv3jyk4/vSSo4WOLdRUfMe45ExJJuhehLYYm0q6Hl6x5WrNq51UytFUIIIUqeI4f35kkCmpuaKZnVYymK9vv60nbiJGr370fbiZPY7+ubo/2M7juQ4d8lSi0VkYYMjRZZkrrq88hRE2jRqkOG26C1oUxk0pAT+zovAnD/SNKySBVcaqRZQzjlfBiNz0Ie/ryV0m2GABBxehvO/ya18GQ5pPtHV/HsiC+g8zji9n1CbIgfVmVdiA3xMybEKdm41CExJird1+PDg7l/dBVObYZiX+dFYm5eMPnFk/r8ew4eRe0Rg/PyEqdr1c6tfNP7yRrBGVWIft456ZmWDPURQgghCs6JYwdYuWoZjp1yP683NzVTituw6v2+vszatBWHlyYYr+usTcuxtbWhU+Om2dpX8jzgMSmqRifXUpFq0iWbJMIiU+klq0uXzWdMTIzJmrypt7m7bz4PfbeQ8OgeVmVdsHVtiZ1/POs27jfuO73A7dz9Xe7tnYtttQbGJDX95DWEKL+fiPhlGwb9Y8J8FmJfvzNhh5ajLVUh3eWgLGzs0n3dUmdHmY6jsXGpY+yxHjNmYobntnHNMiqWt8etb918u+6Qdl5LcoXo9T1sjXOER+zXM7e9DpChPkIIIURB2vz1ahw75U0CmpuaKSmXokxWFJaDzMiSnbtweMm0E4SXJjBvy+psJ8KQlAynTnBTFyP1DYpi+OY1xu1F8SeJsEgjde9nTIw+7VPGTqZBPnVCmxD1AEsrG5xNqjEvxMIQx4ljB9JdQziZjUsdEmOjwXcdwaGBaHUO6SavmmfK8fDkRmPF54c/b+Xxue9IjInGwsqae/sXUK7HFOPxww4uxaFuu7RVovcvwMbKikc+84mJi0fFx1DKuaLxWBk9ZV2zYhnj+67Nt59Dm9Fv4GCtsJkTiaMNONmAo40lNyMUPbdFExkLpXTwVlNr+tbRcvyGgYG79EwbMTLf2iSEEEKIJ3KTgKa+30ru7U2+Rxo5tAe6jllLspOHVae+Vypsy0FmVejt0Ayua3CeHUOqSQtJhIWJ9Ho/I759j1KZBPnUvwgiTm8zzsWFpOBdrvu73N0zl7UrFxsDeEaBu0LlGsaeY+O8l87jjJWgDQ//wcLGDsdG3YyfLd1mCLbVGoDvOtZt3M+JYwfYsmktwcHXQWtNmc5v4lCvPVGVnzdWibb493WtozNhPgtxbNIFp1YDTZ66ZvhL7ub1PLrqaTUY8ioO2rTVoe8/TASgnJ0F1paKAfWs2HvZwCe+cdRwsiAiRknwFkIIIQpIThPQrCyVlJ0kO71h1SlHtxU1lStVTve6ulSukmfHkGrSQoplCRMpez+NFZudKmZavCF1gYeM5uiq2Ggiox7TvYM7vbxacifkBmE+C9MtpJVcRGvJvOlYWcIjn/k8OP7lk0rQvd4jyu/Ev8W0fuLW+je5s20md+/cNvY6f7vrGPuPXaB8hWfROiYNGbav8yLPjviCCv3noC1VHod67Y1DsvX+p43nnVw5OqPiFRWr1czjq5/ko/VrsVRxbO2Ttjq0hQVseEXH2u622FlbsMMvnnOj7Dk6xI74RCjj6JgvbRJCCCFEWoNfG0XkEe8MC4JmJL37rdQrVmSneFbbDl5JSa/vOoIX9wbfdcZ6LLlhrkrUb/ftw+PvTQutPv5+OTMGDcizY0g1aSE9wiVc6mE5yWv+Rvn9RMTpbcSHh6CxL5NmmHHkEW/GjH7ylDH1k8j05ug+/HkrlvZOlOv+7pPhyoeWY12+Bvf2ziUxNpoKlWsYn16mflL62GchDg06m84n7jKBu3vmomKj0TpVpGzXiWgdnY1PVbv3HJBu+5KPXbrNUGP7bFzqEB8WzK31bxrXFI4PD2bye/PTfPbRD95MmflevvxMvjt5lMdxMO5QDJfCEnne2ZL3WtvQt46Wx3HwwYlY5rbXseEVW3p8G41ubiQ1nCzQG7TMGPF6vrRJCCGEEGm17eCFTqdj3erlBP97L9W2dVs2f72aJfOmZ1jgKiu9vdnt5U05rDovZKXXOr/08PQEYMnOlQTfDqVypcp8PGQgfdq159GjyDw5xui+Axm+eQ1fdjMYR98N/y6RCYOlmnRJIYlwCZZRxeZ7++YTf/eGyTzau3vmEnPUm7vhdyhfuTqTJk4zqRqdusCDY2ln7h9cTJmuk58E7z++o3yv99IksvePrqJcz/eMQ5ohaV5MekW07h9ZZawmDf/2MsdFU/WdPcSG+BF+aDlObYYan6omJ8LJ7Vuy8CNUvB5LG3scPLyMFa3hSaKeXDQrNsSPMJ+FAIwZM9F4buUrV2fofyfyUvdewNU8/7k81CdQ3t4C7y46k4JY2/+2xNEGAh8qRh/QExkLDtagFGhtnZkxRCodCiGEEAWt08s9jfdEWU0eszKkOvW9VfnK1U1W3dj89WruhN5Aq3PAoH9MBZcaxp7o9OYeZ5e5K1H38PQ0JsQZyU3V56dVkxYlgyTCJVhGFZvv7p5D+d4zTV4v3yspUd187AIAjo6OREaaPpFLGbDv/psM39szh8Q4PVZlq6DiojOs/pz8FPTEsQN8sWwuMXFxqG0zsSrrQqmW/bGv82LStvdNiyQkLZlUxWRN4LBDy6n8xmrjU1XTZZ2sKdt1EhaWGh6e3GisTJ2UqPtQvtfMNNdj+ZI5JMREUb5ydd6e8QkAG79ezZpPp1OlelVmDnyJThVr5dnPxdEG47BoSCresL6HLb22RTOumTXfXIw3qRo9cJeeyBiDBG4hhBDCzLKaPGbU29u2dVtGDu1hksSmXG0DTJPtqilGuemrNGXZotkoIDEuGqsyVdBXaZrjXtyMeq2DQm4wqG97LCw1PAq/g2NpZ+Ofc5N4Z1deVH1Or5q0KDkkES7BMgpwKk6fbsKaWQXE9J6Cxh5aitJaU7rjaMIOLsuw+nNsiB86h2dYPH8mljoHyvd53/iLIfzQcgA09qWx0FoTuvoNDBF30JaqQEJsNGU7/teknQkRd7m3bz4anT1tm1XH0sYOB49uVBmwxFg9uvSLw3BqM/TfolnBWNrYo2LTP29DzGOqTk7qcf7ss0UoQzylu08xnuPbq5bxcbcO4BSSJ2vRRcYmFWtIKeRRIgkKlp+NY/8AO5MkeWsfW3pue5Dt4wghhBAib2W1wFV6vb1tW7flxKkTmfYmp9uR0WUCYQeWoLTWJlPQwg8tx65O2yz34qbsPNDo7DO8b4syKJy7jkMbGcbDU5tw7jqOUgU8fFqqPovckmJZJVhGRRi0tknLFSUXoLq5oAe31o3BsfTTiwekV/jBqcskbG1twXcdCVEPCTu41KTwQdjBpSQ8fsid7R8QmwAW/wbwlPso22UCD09t5oHPArS2DknFsibvpuzL47DUmj7LSVor2IHYW5cp+8p0qk7eQ7me7/H4z8OErh3FnW0zsbDUcv/YWuzcWlGm42g0js6U7vBfNA5l0r0eKXucnbpMItHK1qR99p0n8vHegyzfvIb46Hs4WCuu3rrHB6tX0Gb0G9n6mez39cXBGpPiDVsvxjPzeCz7B9gRHZ82SfasqiEyNluHEUIIIUQ+yG6Bq3Ub97P/2AXWbdzP//53NtMCWpCUbKf34D4xJirdeyj91dNJo+IyYVylw3MkVSbvxsqlLvd8Fpjct4UfWg4q0bgyyKOzO41/flqb80NS1ee090RS9VlklSTCJdjg10YR+UPaSoedO3fjgc8CHvy0gTIdRxuTztgEZVItMHUlwTshN9INzI/C77Bu434sEg04tR7C/aOrCFrcm/tHV+HUeggkxmFpa49Dg5dQsekPnzY8/AetVkOZrpNNgq2z12QentpsklhDIuW6TzFulxD1AAsra5y7TDCeC0DQol7EHPXGXmvB/YPLULHRaRP1Q8sp1bK/aVsi7qRpX1xMBJaJMUQbLBjXzJrKjhYkKrh9/wGNh/bDx/dkpj+P/b6+zNq0FYOyZOhePcdvGIhPUMw6HsOGV5KGSj/vbJluhUNHmyz9yIUQQgiRjzK6t0q5GkZGFZgzSnBTJ7GOpZ25tW5MUkfF+jeJ8vuJ2BA/VHxM+lPQ7gdnaT3hlB0a0Vd+Jv7uDRzqv8T9I6sIWtyLu3vmYlurKQmP7xuPk9EqIVlJvHNLqj6L3DLr0Oj4+HhmzJhBaGgocXFxjBkzhg4dOmT+QZEnnlaE4eefT6RZxN2pyyTj0Jojh/emW2jr4c9bTYpZxYb4odHZ06NDfTQ6e7SOzjw74gvj+zE3L2BVtgplOo7m/pFVWGY0DEdnR3TEfZwzSJKDFvfGqqwLTq2HEH5gqUlQTndN4x5TuLtrNjqdrXEuS48O9SndeoxxjeGkIdWmBbViQ/zQlqpg0obYED+i4iDMAsY1s2Lt/+LQWVmwf8CTNYCHbEg656cN1Vmycxfa517k0enthCdCz23RRMaCvXXS0GiA91rbMGK/Ps0cYQf70ln4iYvskPgkhCiMJDYVbhndW0Ha1TBSDyHOSgGtE8cOEJugKNslxSoYB5eSGB9nHNmW+vOWNnaZLucEpsO6I05vo2yXCeiq1Tfe18XcvMD9o6uwKutiPE7KP2fU5vwiVZ9Fbpk1Ed6/fz9OTk4sXLiQBw8e0KtXLwnmBSyjUvuPwu9Q6ilzXNav9U630Na9vXNNClDd81lAov4xVmWroC3zLGE+C3FONXfFqc1Q4xNLRw8v7vksNF1iyWch1hoN8f+uZ5w6ILvtDQAAIABJREFU2Fo5VzEm1zE3L2BhbWuyXYZrGhvi0FdpytLFs1k8bxpanQPx90Mp1bL/v0tHBRP5x3cAOLUaSGyIHw8PLcXSEE/MzQvG9kX9sAyX0vaEPIhi72UDjjYWrO1uWuxqU0/oudqbt70/o3Klyrzdt0+aSoiht0NxuL8bjQWUsrXgm95Pkt1h+/RoLS0Y6G4FwBs+eq4/UDxjAwasOf/V2hz9/EXGJD4JIQojiU2Fz5HDe1m3evlTC1yltxpG6iJaWVkuafPXq3HqYvpw37nrJO7umo2ysEhznxXms5CXX+qRpfm6KRPxjO6d4sODKdt1EmEHl+LcdRLPNO9r/HNWlnjKS1L1WeSWWRPhl19+mZdeesn4d41G85StRUHK7KnkneDr6RaDSIyNBt91BIXewMLaFsdG3Y1JZPih5Vg6luXu7jmo+KRK0k5thmJf50VjAmvn+gL6oItJ28TpsbC2xaVyZUICAyjTdSThh5abLOt0z2cBiVER3Fr3JrauLXl84Qe0ForIH7yfuqZxcrGHaL8TlOv5nun+4mKp0GcWhsgwIn7eyqPT24n8fR+Ojs8wduw7QFLV6ODQQKpUr8rHo3uhD3nErG1buRSW1HOb/jxehZ2jA5HVWjJr01YAk2TYxsqKxzGxONrAN71NE+kNr9jyho+evnW0VHSw4HEc6LRQqbwL3y1elkc/dZGSxCchRGEksalwOXHsACtXLcOx09MLXGWliNbTRuplth9liEVbqjzxYcHG+6wKlWswceK0LBetSpmIW5VJv6dXq3Pg/sFlOJZ2Juaot7FqdMolNlO3OT9J1WeRG2ZNhO3t7QF4/Pgx48ePZ+LEzJ8eWVpa4ujomO1jaTSaHH2uoBWWdo4cNYGly+ZDpxRPJY94M2niNBwdHalYpaYxQEb5/WTsQdXqHBg5agLr13qjXhhu8sTSrk5bHl/4AV21+sTeumJcrzfm5gXu+SxExcZyZ/sHWNo6UL73TONxQ30WoJQi4uet2NZqmjR0OSwYCxvTRPuez0ISY6OZ/sECAD5f8Sl37t0GrTVhBxbj7DXZpCeaxATKer1tOmS6+xTu7plL9NVf0Af8ZpJ0Rx7xRqfT0enlnjR/uS+1a5SmtvYS2uvBPPS7x7RvvsXRWuFsZ4FvUIIxkYWkOSvPOloQFR9JxOntONjZMHfzJgZ37cKeEyd489P52FsrrG3SrxrtWVXD9QcK3dxIHK0hOg6GduvGJ2+NK6B/EaCx1PDMM+b/t1lQshufchqboPB87zNTFNpZFNoI+ddOfYwBW1tbbG3BxtoGWztbLHPxvS0q3/ui0s68UJD3TgWtqHx/U9qyaS2OndL29G7ZtJbuPQcYt6uQ4r4pWWyIHxWq1DQ55+49B5h8LrWM9mNhbWe8r0q+Zxk5agKdXu6Z4b5SX+/uPQeg0+lYv9ab+PBgwr5biHO3d032OXXGnKfuMzV9jAFra0tssSPB1pbYEhKTUiuq7S7uzL580u3bt3nrrbd49dVX6d69e6bbJyYmplm/NivSW/e2MCos7WzRqgNjYmJMn0qOnkiLVh2IjIxk+BvjWLp0Pnq3NkT5ncA5RcK4dOl8ou//Q9V+pk8s9VdPG6sZ3j+yirt75qJio7GwscP++Rexf86Tu7vnGLeBJ8np/SOrKNNpdFKyG/XQ2NucPG8labt3ubtrNutWL2fwa6PYtO0Hzvx8DO+lc4l8HEX4Ye+koltOFXFqM5Sw7xanP2Q6LpoovxOU6znD9Bdbp3F8Om8mMTEx1H/RC71eR6xNHPF6PfpoPfPeHMf0z1cQFq0Ytk/PhleeDG0eskdPfIJidz+7FHN77/P20qVs+f4wjv8mwPbW8IwN6SbSyQWx6ru589XMDwB49Kjg/q0884xjvh2vXL7sNfeyE59yGpug8HzvM1MU2lkU2gj51069AfR6HXq9Hsu4WPTRev6fvTMPjKo82/7vzL5kMlkmASWEWqvWqGjtqy0vyCoiS8KmIhSXChaw5UO02gWsVaG1bCFFBQWsFTWAEDABwqoBQ9HWFWmo0rdIFiTJZJ3Mvpzvj5M5ycnMICgg2PP7BzJz5jlnTibPPPdz3/d1hb/G3+3Z/Ls/k5yt6/w2zE3w9eanc8n5/Pfb2Vaos1duogq5qqr/KN7LT+66P6Fv8ISxg2LGTUS8cepLFiD6PTTuXoG9zwRJ22ToTFY9X8CP+8aWzXd+L8np3RAjYVxNTvn8K1/arDgu3jrwVPGGIBDQ4w17CLevl/4b5qSunI3rPl/npwuJbzQQdjqd3Hffffzud7+jT58+3+SlqMSha/9wVO2wruZzuvX8LgNvGsjOnVtwjP5NzE5o4I0/xs0YN+5agf1/J5A2dDq+yoOkjZ2j2NVM5GEcbKyWg91oUOwsLcDgyJbFrKJ9v/SbKpcl5Y65kx/3HcLygvns2CH160QCXrTW1IRlP/q0ngQbqhJ6Ci9d+jQDDn7EvCWLFM9HS3OeXPUC9W4feWs9uAPQK0VAANbeFuv/O+LV7WRaBQrHdwTNt73uYdJGL691emxSkReXHwwaGDdw0Ff+naqcOur8pKKicj6izk3nnqitUDyhq1MRuIJT9w1euvRpDh/6iBmz5sS9ls7jJGpDA7Bc0TfGuzjRe3FuyydtxINgcyjKuhPpyCS6R/E2ClRUzme+0UB4xYoVtLa28txzz/Hcc5LY0cqVKzGZTN/kZanEId7EWbZzGSFvW/yA0dtG7do5aCx2BK0ex8jZyrJkJBGrkMvJ8dUPEGyoRp+ehWC0xA9O07PksaNBsWP4LBp3r5ADYX91BdqkNKl0uukEBUvmYTKZ8Pl87N5dCgYL3cbMIeRyUl+8gIjfTX3xAjLyHo0xng990JDgOiSF67eK5rHo90ZW/vEexXuP9qo8sXolu/bvovhOE/2ytZjmu2LKnatbIxh0UO8RmVnqY85NRiZeo2fD7RZuW+9h/HoPzT7kbLHNCN4AzFn+rNoPcw5Q5ycVFZXzEXVuOvd0thUCpdDV5HumsXzFUkUrWX3xApJMBsr2bFUEg10Dy3gCWo7cR9i+eT5XXn1dwkAyOs7Uu/Pw9rwB75EDtL7zOvr0LCw5A2k5sA6tNVUOxjsHqVqTlfQuCQzHiNk07l4hCY92EfA6FU62UdB7gBoMq5y/fKOB8Ny5c5k7d+43eQkqp0iiLwFv0bz4Ygop3bl46nKOr5pB+q3K16UPnyVZJZmTaX57jUJpsG7zH6kvWUBGrjI4Tel/tzy2IihuqEIMh6TXFs1H0BsUPTL5S59GJ4aJ6M042q/DXbEXjd5IRt6jhFxOuWRam5yB9apB+CvexKTTxg2SowrXYtDLG2+8wbAf9eCOq74Xc78en3I/119xBfe8+hLVDa0x5c4zt3l5vSLE5gkdpdJTir0A3Jajo8kHgiCVSf/iRgOPDzDKVklN3nDM+VTOPOr8pKKicj6izk3nnpMJXQ0cMhKTycQzBU9T65Tar1IH3YeuS3b1dMaN+D1fGoyW7dlKbfVRtC63oj3NWVpAuKVOLruefMdQWtqvK23EgzRsy49feddQrXhfp8PJNgoWnOFAuOitN8lf86KsEj39NlUl+r+Rjz/+mEWLFrFmzRrF42+++SbPPvssOp2O8ePHc8cdd5x0nG+8R1jlwiChSmHAi7O0QDkJb8sndcC9CFodoZYTCeX3hfZgtPPEmTnmN9S+/niHanS7IJblir74jh2MCYoFvZnKRWPRpXRHY7LiGPFgTF9v3canEEN++To6e+MBJF09GN+xg9RtfApz1T+4/xe/ZOCQkZTt2UrBknmEfG0xCtfa5Ewcw2cxP/8F7lj1OJvf+5DFu5dR80VNJ3ukjuzwhj075L7h6tYIf/04yBt3KkulV+eZmVnqo3uSQLIRUk0CL442x5RTj17roaR8nzrxq6ioqKionAO+rPx56K1jWPV8AaY75yuO+bLsaqJx9Wk9qTtJMBrNwApGC45O65lotVxd0TxF2bW904a+NjnzpJV3X8UDONEasbL6KL+8Lw/n8c/p2asncyfeytDul57W2MXl5SzZsJGaL2pItadgpY2/5unol51EeaWb+155QT52RScbJTVA/vaycuVKiouLMZvNiseDwSB//OMf2bBhA2azmYkTJzJo0CAyMhJ3U2vO9sWqXLhEe4LzhvRGa7Lir65QPO+vrpCErnIG0rh7BZWLx0nB5HdvoOXAOo4tyEPQmxO+TgwFYoLkkMuJoNEhBqUgWAyF8BzeR+WisdRtmo8lZ6AcFDu35QMCyX3uoMe0lYRdzvjiV0GfbKEEJ/EVDvqorTlKwZJ55A6+RgqCvW1ozHbSbp6uOK8Y8BFyOak6Vsnre95hbvEegv1n0PPhIoL9Z/DYmkKKy8sBKTvsC8G8QUZmlvqY8oaPtkB8ZejDzggTN3r5xY0GjrWIcY9pC8DCl/9yer9MFRUVFRUVla/E5Hum4dq5DN+xg4jhEL5jB3HtXMbke6bJx9TVfB53bXGygHbyPdNwlixUjNtQWoD58j4xwWjnNVnBknnoruiPGPAkWM94ef/9d7HdMpOwu4kvXvp/1K6bC4KGiN+Ds7RAcU7ntnySf3Sb9P+Shfzwhz86rfsTDeg707y/EK01BU2/qfR8qAj/j6bx0IpNbH7vw1Met7i8nMfWFMrrqzAR/pqnY9AlOvRagUGX6HhxlIYlr75EwSsvsPxmN745SSy/2U3BKy9QUr7vtN6HyoVBdnY2y5Yti3n8//7v/8jOzsZut2MwGPjhD3/Ie++9d9Kx1IywSlyBA0DR79G8vzDWpH1bPklXDcJTUSbbDFU/dy/e//xDLndu3l9IfclCMjq/rrQA0e9B7+ip2JV0V+yl+e01ZI5/TNFnY/7uD3F9uA2N3ozn8D65DyZ1wL1oranUbXwSd0UZiCLHV80g5abJir5hwWAm7GmVDd8TiWQJRgtiwAvGJIRIBMt1I9Cn9aBhx7NS+XRLreK8DduX0bNXNr9Z8Tp+jLjXzUWfnoW9zwSShs1iyYblsk9wVnoyWckhDj2QhP3pVjISWCwlGyEiijw+wEjBu4GExzSep6qaKioqKioq3zZOxd/3VEWzuo57+NBHbN88n4jfgz6tp6RT8uk+Js/osMWKK3B1kuxutx6XUFfzOWkuJy3lryqsIOuLF2DIvISGN/5I2OfGnJxKxNtGw7Z89Gk9sfa+hbK3y07ao9yVzv7D0fO0fbiFjDFzupRLP8jiHcvIHTX+lMZdsmEjScM6Mt6tzc3MLNVw2BnhSoeGOTcZCUVEmlyteIIws1Qj6628OCrEjA2Falb4LOL+4gRao/GMj9vk9/Hzn/9c/nnChAlMmDBB/nnYsGFUV1fHvK6trU1hB2a1WmlrazvpudRA+L+cRAIHOsLYbukoMzY4smkTReo2PokY9CMYTIgBH77Kgx3evg3VMeXOUXuj+s1/IOJ3ozFaifjcaExWTNm9FUFy89uv4BgxW2mdlPcodUXzQBQJtzWgTc4AUSTUUk9T2V8wX3ojgt4UU5otRsLobA6cW/MRw0GIhIgIIrWvPw7hkBzUh1xOWvYXSj3CSWmk3DxNet22fNyH9qAxWiAc4OKpyxG0HX8uYjhEqPkEQ/NG85cNZWTkdUz+DaUF2Pv9hLovauTjH/rJvdz10nOsGSMJXz0z3MiUYi+r88yd7JS8iCIU3GqmvDKMQQOTiry8Ns6s6CP+xY0GCt4NnKuPiIqKioqKyn89X6agHC8YdO1cxowZJ/d5njFrDldefZ2ckDBX/YPJXYLseD24juGzcG5dQkNpgSLQjZ7zlb8+T8P+QkUrWNjdhMZgxnvkXeyO7vzit/NY9XwBtuEPK4JpX69rT0swK95GQcQfP1tdVXfilMYEqPmiRi65dle8RWaShmXDTfKa6N7NXjxBkeI74+utHPnCecrnUjl9tEYjlTvfPOPjXn7neIqKik77dUlJSbjdbvlnt9v9pb7kaiD8X04igYO6jU+RIk8+e2ne97JCOMq5LZ+UoTPQ2RxSGc+lNyCGgoSaY3uCU/pOpPXAerT2TEXAWl+8gIinRe4HBhL4+noRjBYEnR7HiAc7rmHrYtoO7aHbbY/HqB/WbXwSjcVOJOSn2+1PKM4pBr1E/B5qNzyBxmCOK4jlGDGb2td/T8TnRtCZ4u64pnS7iF1vfxTT55w+fBYN25ehNVooLi8nr18/eUfynldfwmaErGQN8webuHuTl2a/iDvQoQ79sy1ewhEIhiCMpCgd3f2cP9jEbTk6/vC2GgirqKioqKicL5xK1vhkr40eF63SW/KH38hVeol6cMNtDaSPmC2Lftod3RXnXDz/1/K6KrqW6xw0P/2n3xNuayY7gRDY6VgixVPEjrd26pHZ/UvvR5QeF/WQxwgdWMP6cSaFbspLY8zcX+JNqLdy2UWOUz6XyoXPpZdeyrFjx2hubsZisfDee+8xZcqUk75GDYT/y0koghX04a+uIOxuonHXciJ+N427V2DK7o2v8iBhl5OmPS+QNnQGlpyBtB3cSUbuI9RueILjq2bIZcT2PhPQWlMRDGaScgbg372UuoZ67OkZ2K69hdYPtpI5dg7GrByOr5qRsGRZozfhGPWwMuAd+TB1G5+MHzyHAgiChszRv47JMDdsX0Yk6EMjaGLGTB8+C2dpASn9foLGaJGVpZ3b8rFeNRjvZwcINlYhGMyYDQYq674ge3wc+6jmEyT3uYPH1hQCyMFwbr/+XHbHbUwq8jLlB3r0Wpj1IwOvfRJUZIfvfcNLq08kEIZlw02K8ui3joZIMpyhD4CKioqKiorKGeF0fHfjEa9K75lnFiHojFQuHos+vSf2PhOw5gzAX12BxmilYVs+GqOFEaPvVHgPDxwyklXPL5XXVV2FQk29euMY9Qh1RU/FXXtpjGaeeWYRKcNnx1gincp7jJchd+9cylOjhpzy/XjotvE8tqYAhs2ipaGeftlJiuf7ZWs52izGPHbYGeG+LRFmTZ54yudSuXApKSnB4/EwYcIEfv3rXzNlyhREUWT8+PF069btpK9VA+H/chL1tNgd3WkqWUBYqydjzG/lft9owNs5gyqGw2TkPoLns7+hMZhJv3WmInMcCQYQAx7Mh4t5LU/TrvTXxqTiYlr8PvncKTdNlvt4FeP7PYQTlNiIQX/84FlnIOxuonbdXLRJaaDREm6tR5+WRahZshGIl702ZuUQbqmjcfcKMsfOlccNfPFZzHt3bstHMFho3l8ol4BHz6+1Z5La/y58va6Ve4WfWL2SzWU7AahtE1n29wCbJ1iYWepjdZ5SHfql0dIuZ71HjFtC3aYmhFVUVFRUVL5VxKvSSxkuZXyzpi6Xe4MDzko8FWWkDZ0hu1m8X74qZryp0x6Ug9GEQqEBH3Wb5stJCVnLRaMnZfjsmIrBUy2Zjh7z8l+fp6pdNfqp6WMZ2v1STtUEMqqzsmTDcqx6MUY35Ym9fpKNoH2yVe4Z7p4kYDdpmTX5Z2p/8LeYrKws1q9fD0Bubq78+ODBgxk8ePApj6OqRv+Xk0gJceq0B9HptHLPrqDV4T1ygIzcR+SfoxnUiL8NY1YO7ooyuUw4+rxjxGw0Wh1JRoEp10SYWerDNN8lqSdfE8Fm6pjQrDkDSLnpLuo2Pknl4nHUv/E0obZmAAS9Mb76tN5IfRfFxfqSBaA1kDn+d6SPeBAEAcfwWWQ/XETa0OlorClYruyPPr1n3DF1Kd0R/V7FF4av8mDMe3eMmI3WnEzbh1tiFB9T2y2ejFk51HxRwxOrV7KzfCf/70Y9PWwCVoNUBj2z1EdFvXRfCj8JyueL7nK6/DB/sEm+b/eXeGn1i9iMwhn/LKioqKioqKh8dTorO0+9O4+yPVtP6/WJlKdDLbUda4/hs2j7cKts6Rg9Jp469cAhI6Ue5fJVCDpD4jVPKEBd0TwqF42lfvMfsFx6AxFva0IV7FN9nwOHjGTRi8VUVB3m0N7V3D7kx6d1P0AKhsuW5vPU9P/HfVsivHU0RDAsMvdNH6s/DFJ0hwXfHBvLhpv49R4fEzYG+d39P1eDYJVTQs0I/5eTqKcFwNPSiKPTJBjdTXRX7KXlwDqCDdXo07IQg34qF48FkbjPh9sacSPy8sdB/jqmI7N5z2YvbT6RtE7Xo7M5QNCg7/Zdwq4Gkm8cI5UjN1TFqk9vy0cURXS2dKnPOOiTfPBEyBwjlUQfX/1ArABX7iM07l6B/X8nxIhMRPuWBYOyLzjRTmqo+QSC3kjt2jkIeiOC3kTakPsVqtU9LupB8d5dDOql4dm/B7AYBGb9yMCLHwYVog9RgYeJ1+gprwxzSYpAvUeke5LAh9OslFeGmVTkJRKB0UNuOSufBxUVFRUVFZXTJ5H4KMQvJY7Xf2tLdcRtL4t6/IK09oj43IqxOqtTdx33hz/8EV5PGwjamKo757Z8zN+9Ac9nf4vRS9EmpcWtuLOlOuKWb69cvhhXk/NLe4m/KtHA9udF6/i0pg67ScPG22Or6e7ZqlODYJVTRg2EVeL2tEy9Ow9dSnfFJKhPz6J5f6HCLslfXUF9yUJSB/6Uxl0raN5fiPufbyom2vriBVhDzdx9rV4WfrIbodknCUQ173sZe99JiuA21Hgc2w9zFedq3l9I3ab5iAEPOnt3xGCA5BvG4PqgBERwjHoYa84Aji3Ik4PWRAFssKFaDlYbd60g2FAl74pm/3KTfC6tOVnaiTWYY0qg6994Go3VTkbuo4r36ix9hqa9fyXsqkdjsDBx4E28tqOKtz4Hh0VgZa4k5PDqOHNCgYdoj7A3AKPXemhrF9PyBuDOW4bx+JT7z9bHQUVFRUVFReU0SSQ+Gq+UOF7QvGzpHxG1WtI7i4JuyycS9JN+c4dfcTSL2/jmKpzbCiAcRGM0c+uto+OOu6NkIaIokjlujqT70u7yobN3A1HE9/mHJF07TH5cn56FJWcg7oqymMDZtXMZeo0W283xy7d7Plx02r3Ep0Nuv/78ZMRIWltdXD7hdvplaxXP98vWUtOo2kuqnDpqIKwCxO4g1tYclZQIO2VMzZf1wfVBiaJ3tnOGVQx4cb1fgu2HuYoJNenaYbgOrIsRhJpS7GXSNXpWffA6lQfWo7HYEUXQWVMINZ/A+9kBhbBDav+7MPe6lrqNTyHo9KTd9DMsV/Sltf21LQfWYc0ZgNaaSvP+QrxHDiT0Fo7urkaPb9y9guQf3UbjzueoXDQWtHo0Joui37m+ZCEgqWA37y/EV3kw9l7kPUrdpvkKdeu1Gx7HpINWP7gCoizkEG8Cr6iPkLfWQzgC/hAYtBAWBSYPu0UNflVUVFRUVM4zyvZs5dU1K6mtPppQfbkr8YLmiF7SWIlxwSiah9aaihgOyWuRtMFT0VpTadi+jIvbe4f3lOazf39ZrM1S7iPUbXwKY1YOglYnr4XEcEiu5nNXlCltKEsLCLfUYbDa8e1eRl1DrVwxuOQPv8H+JeXbp9NL/FW57CIH5ZVuRc9weWVYVYpWOS3UQFgl7g6iZvN8dDYHKf3v7vAINphjemehI8Oqd/Qk6KxSTKjN+wtxfVCCzUiMIFQ0A1o43syYTQJtEQHbtcNoPbAewWAm2FCVQCDLB4Bzy2J0b7+CxmIHIOisoub5+wm3NdJ2cIciU9vZW7i+ZCFJvW+Rv1Si9k/Nb68hc/zvZAXrrl9IGbmPUFc0j9Z31qMxWhED8e+FGPAoXqcXg+h14AtBslESd7jSoYkRfSivDGM1gACEIgKfrX/9zP2SVVRUVFRUVL42nRMHtlQH/rBIyvDZ6N0r5Cq6jhaxKnSmJMr2bFUEhfEcO0IttQnWPN6O5EJaFhF3M9acAYjhkCL4TBk+m9q1c+IGqVEnkBhhUYMFAEdXNenhs6gregqz2czUabGexvHG6lq+HW8D4Ewy/baJ3PfKC7w4KiQnWFSlaJXTRRXLUlHsTEYn1KQfjKK+ZAFaayoX3ftnuk2Yh+j3JBSY0qdnYcrujWAwyxOq59P9eCrKyBw7F5efuBnQaGbU5XKT1HsY7k/2oLGmkDlubtxzNe8vRGNNIe3m6WQ/XET6rTMRdHq0yQ40Vjvpt85En96TjNxY0a7Gnc/RsH0Z1iv64q4oo3LRWBq2L8Pe7yd4//O+QhjsZF9IvR4tIeL3oE9LcC/Seso/V/5pFHaTwOYJFvxzbRTdYWH1h0EuSxOYUuyVRR/eOhpi4kYv7gDo9BYOr1WDYBUVFRUVlfOJaOKAflPp+XARnohWVlaO6o407VtD076X29cpm0gf/RuWL18qC0qV7dmK1mSNWT/o7N0SrK96cvGU5+j1aDFpQ6ejd/Ts9Jwy+BT0JsUY7oq9HF81A4C6TfNp2rdGFvZ0liyEcAgxoSuHD9PNMxXXDvFFVp3b8rH3maC47mjP8tkit19/Zk3+GTN2WzHNb2PGbquqFK1y2qgZYZW4O5MpfSfSemC91D/bWIXGaMFiT0N3eZ9Ygan2HuGW/YWIwY4saWfPOquBuBnQaGY0OSUF14dbEcMhMsf/TvGl0vlc8Uqzo6VDmeOkx4ONCSwCgn4QI1gu/19SB09tz1ZvoWFbviz0FUWfnhV/x7M9yNWnZ2G+LN69WEBS72HSfV03hyQDFI5XZsJfG2dmzDoPLj/yv0kG8ATAoNPyj7+8fKZ+tSoqKioqKipniK4lzZ03za05A/DXHMb1fomcxY16/kZLhQGWL1+K5boROEsLFOXIEb8npie3awWbc1s+KTfdJQefqQPula/NX12BzWajuTSflOGzCbmcNL+9Jma81gPr0dozMVx8Bf7j/0ooiqVP7ymXOa96fqmifW7gTQN5v3wVVTWfk5zeDW04qCjfdu1cJguvnk1y+/VXA1+Vr4UaCKsk9BLWOyTj9mh5j08UET/Ygu36UXKALBjMiKEwDduWgijKWVxTr94KoapwBO59w8tLo2N7hCcVR9D3/SmRbUuyf/CZAAAgAElEQVQBIeZLJSqQpU/rKZcjd1am1lrTQIxQu24u+vSshJO61p7ZqdS7CsFgIf2WB7DmDOD46gcUr7H3mZDwCyn6fPO+l7HkDOx0LywYul+G+59vYu51LfovDuIKdGTCCz8JMv9tP4edESx6sOohEAKrAUIRSLZa1CBYRUVFRUXlPKVr4qDzprm7Yi/e//sHmePmKtSXASxX9KWy+igrly9Gd8UgvEcOEG6to37zH4j43OjTe5J2888AOtrRdAaSrrkZ75EDtL6zHq3RghiO0LAtH21yJmIwQMBZScuqB6SEhcHCNVf35vC/DtOwfRlhdzOZ4x+LafFylhaQNX01x1c9QEbuo4TdTTGb+g2lBaR0soGsdZ7AdOd8uX2urD3QjZZMR8vFO7uPnM3+YBWVM4UaCKsw+Z5psuF6Z6EEy6U30Lzv5Zjsb+vfN0E4iNaeiTVnIK3/2IzO3o2wuxnz5X1kmyN9WscXhC8E8wYZY1SjC94NYBr2MFprOvr0jnKf6JeKu6JMCoLTpaC8qewvVD93LwAZeY/KO57R3t7ojmn9G38iY/SvFGrOAGIkTNrN06kvXkDqoPtk0Qh7nwk4t+bjGCkFvlprKpGgX7JlCnjR2tIREGg9sB7P4X3Y+07EkjMQ1wdbEP0eBKMFMeAncPxfiEE/dZvmg1/EZpQy3yfaROa86VOIhU3c6KXBLdIt1c6e51Z/I797FRUVFRUVlVOja+Kg86Z5y9/WKQQ+Tb16kz58Fo27V6C1pqJL6Y6rrRXNoTfltUZ0XWW+vI+8HrHmDJAyvqUFpA2dDnQIWwkIZD+8CUGro3HXCtoO7lTYSh4qWYi19zBS+9+lcNCIYszKIdxaD0CwsUoW0AJlAJ5+60yFwKgupftJ1bDjuY+oqFwIqIGwisJLuLL6qJTlDXpxV5SRMea3sQrRu1aQNnQ6DaUFRLwuNAZJ6TAalBovvkLK4vo9clBsM0JWsoZDDyTJ533raIiRr3kIvr2ahuZmbElmxMsH01BaIEv3Z4z5rdJGIBRAa7TKQlbxfIIdI2bj3LKYuk3zEP1edCndSR10nySUVbyASMALkYjkWdyONWcAAWel/BpBb0JjsmK9bjjuQ3sQNFrSRz4UE1hHM8ruir00lCzEpgnhAmy48QAuP0zc6CXJECsWVjjeTN5aD7+ZMv0c/JZVVFRUVFRUvg5dEwdaayracBD/nmcINnyRQEy0Ss6wNr/9Snwhzk3zMPe6tmO9s3UxqQPvk8eRhK3MmAyGjgRD5UEych+JUYhu3LWC1P53naTFKwvfsYMIBov8vDVngByA12+er1Spbk8cdH1fZ1sM63QoKd/Hig2FHPnCyWUXOZh+20S1ZFrllFADYRWgIxiOqkcbs3KoXDw2/qTeWC3vdNZtmqfo2RU0WprffkXOkkbczdRtmo8uALmvedDroMUHdpPkiZtiFijMC9Av2yZlSYtKCTl64/pgC5lj58QEuA3blyl6chL5BIfbGgHQpXRXWCdl5D1Kw/ZliEF/TClQ28c7SLt5OklXD5bHEsMhXO8Vx5YX5T1K/aaOL4uGkoV0SxJ4bZwy41vnFmn1i9S544uFuQMwbtBgWltV3zsVFRUVFZXzmc6Jg2gZ8MwHf0PumDuZMHZQ3MBTY7SS0v9urDkDcG5ZHF/DxO+V1zdSRZpPEYw2lBZguz4X94dbcO1cBrfMVKx/OreLCToD7oq92PvE6qzUlywg4m6hcfcKkq4aFKvDsnMZtw7Lk/t/M3t8B6MQoWV/IQ3blqJPz8LeZwJaa+pZF8M6VUrK91Hwygu8OEpDv+wkyivd3PfKCwBqMKzypaiBsIpMVxGIzv2+UTqrFEYn786TujVngNQLs2gs2Q+uw12xl+a3XyHod5NuUgaKY9d5YoSkCseZyFt3ENEvxv2yCLXUKnY5E+146lK6y9560R4da86A9jFOAAKOUQ93KgUyojFaFVni6Fhi0Bf3WiJ+D41v/IGQz43NCK+Nk97LzG1eXv0kSLMPbEYpK5xshDte97D3WJhmH6SYYEAvLTbj1/ylqaioqKioqJwzEpUBT75nGs88s4iU4bMVlWxpQ2fIm/FRZejomiW6RgIIe1tBFNGYrBguukzqH/Z70KdnkdL/bixX9JXas3wevEXzEHRSdjjsboppY4uKallyBnborKT3xHpFPzz/fhfzZX3wfnaAUEud1AIW9NKtxyUxvb1le7byzDOLSL91pmJsbTjIzAd/cw7utpKS8n28ULSOT2vq5Mzvig2FvDhKo1hLvjgqxIwNhWogrPKlqIGwCiBNdl3N4O19JsSoGnYWUJBKdUwJvOlMQEdg3LxsjBwogjRRdRaSiiJlSSMIBgvN+wvxHjkgeee1qzTr7N0Uu5zJP7otRtQqqqQYtU6K9uhYcwbIvnk6W7qiFKhx9wrMl/WhvmSBwn+4bvPTCEZz/PdotJA2+re4dy7F1VhHv2wtM7d52XA4xMY7LApRsM+bRd6piSgen1TkxeU/a79SFRUVFRUVlU509gDO7PEdJt8z7Yz1tkbHWfX8UmqdJxD0RtBoFZndsLdVbhmLp+rsLC3AfFkf3BVlRHxuev2qRB7fd+wgaPVoTFb59c5t+QgaXUxvsmPEbOo2PgmCgBj0yTor1pwBiJEIbR/vICPvUUUmOHovyvZsZeXyxbhcLkCUnTy6jh1VwT5XvcGJMr+VTT76ZdsUx/bL1nLkC+c5ua7/avw+xM8/+6av4muhBsIqLC+Yz/YdxQAcXzVDLiWW+2bbdwsFgwXb9aOwXNFX6iMpWYgY8MUEy87SAsSATx7fX10R10c4ap3U1VIpyagl1LN3jAhEfclCIgE/Wmsq9n4/kcqImk+AzoCztIBwax2Czkhae99uFGNWDkFnFTXP30/Y70H0ezBfP6rDjqC0AGvOQNyH3kTQmWSBLMFgRtBoSbo+N65llBgKtotGPIhm028prwzz6idBNt5hUQT8q/OkXmCrHk60iei1gsJGSUVFRUVFReXsEvUAtt0yU1Y/Xr58KRA/mPsqQXPXbHF0jMrqo+jMSYh+D2IoiHPbUiJeV0zblaN9494xfBZ1RfPwHTsYo02CKFK38SnE9vLpUKszoWVktzvnxyhY+z7/EGOP78uK1RqTFWPPq3nlr8+ze/tmDn7ysbQGMloSewyHAtBv6knv35kmUeZ3/OvauGvJyy5yJBpK5QyhsZix/SDnyw88j1ED4Qucr7q7GX1dbfVRBKMZMeBFn94T8+V9aNr7EmIkjM7mwFNRhqlXb/w1/yLp2mF4P5Nk/AWDGeuVA/EefR9rzkC5xFifniUFlZ/sVvS2mHRw5bNtHG0WudKhYc5NRsZ8X8fEjV4Kxyv7ajU/uJ3QkXdiRCAych+hbuOTikA16brhtH28HcfwWVJwGvTFLW+OvjfXB1sAcL1fQusB6X2IAS+ew/sI+9rQGEwK64PatXNI6TsRgyO74z2mZRFxN8vjG7NyqGsXxWr2xc9ye4KwMtfMlGIvABOv0dMvW6tmhFVUVFRUVM4BXdu/4qkfRzndoDkRA4eM5PChj9jR1ER6p41957Z8xFD8tqto768Y8OLcPJ+wz4MupTv6jO8Q+OIIgk6vyObWFc2LbxmZlBajYB1NIESCPoUYaX3JQmrdzdQ3NSnWQHWb4o+tT8866f07Gxz5wkm/7CTFY/2ytTR7w9y3JcKLo0LyWvK+LRFmTZ541q9J5cJHDYQvYOJN1M88s0gqaWlyJgyMo6/TXdEfTVNTbNbV3UzjruWI4TC2a28hdfBUKvNvx/3JbkmESmeASIS2j0pBb6bt4A5FOXF9yQIiHheVi8dJJc2X/g+WT7exMrcj4L33DS+tPhFvEEav9dAWBHt6Js1uD9l9J9HyzoYEu5B+tDYH5pyB+D7/kLaPShEMJpxblsjBabxybvOlN+CpKCNz7BxF5tqaM5C2gzsJ+9oQEGOCb11Kd/zVFYrrEMNBNBY7YjjIsQV5CKJIkgHq3B12SV13Jq90aDjRJqIR4CdFXua/7WfM93Vqj7CKioqKiso5oKsHMCRWPz6doPlklO3ZyvYdxWSMmSPbQjbte5lwqzNha1lU+8RiT8Nms0O/qZh69aaq4E60luQY1WlBZ4jbIgZCzHsNNZ9AMJjjJxqK5sU8brs+N6ZlrKvH8LlQjy4p34fdFL+K8PKLM5h+20RmdFKNnjVZVY1WOTXUQPgCJt5EnTJcUlbu+XBRwh3M6Osad6+IOxlG7ZHqSxbi+ngXIZcTQWcgEg4imJLQGEw4Rswm5HLSuPt5knoPa8+WVqExWon43AgGM7Yf5pLa/y5aVt8XI4r10mgzY9d5+GVfA2sPBbGKRryXDURo3aL4Ioj9guiJLvVi3IfLFFls9z/fRGOxS9ZI4RC1a+cg6I1ojEmkDvopLQdi/f2k0qOnAA1iJAThQEzwbe87kbqi+WhMFhwjZuP51z78n2zHopNEsMw6SDYKFI6Xen+f2OtnUpFXIQo2pdjLyMt0MT7C0R7h6ydPpOy5F87a50RFRUVFReW/na4ewCCtK+KpH59O0JyIsj1bKVgyj4jPQ+OuFZh69cbz73flgLV5f6HcL9x1g76+ZCFJRj211UfR71pBsLEaRJFInFLliLeV9BEPKirzUm66i4Zt+Yrj/NUV2B3daXGeiJ9oCHhjHk/pO5HWA+vlTLKgN6IxJSnGtKU6mHp3XkxlYueKxYu/cwlPzL6D8Vddfsr3L0q0N/iB/9EypdirWEdFM7+5/fqrga/KV0INhC9gamuU4lbQoawcFYqKt4MZneATWQ9F7ZGiO4TeI++iS+lO2O9Ba5I8fMPuJlrKX0UMeDqVDr+Axmgh4vegtdg7ypAb6uOWs7gC8OKHQU60iey5W8OYdesRg1rqiuZj6nVNzBdENLPr/nQ/mWPnKh63XjWY1r9vQmO04Bj1sGJX1F9zmKCzKkEPjQ+NJQXRFwKNjuOrZsjK1PY+E6Qya0GUguDPygkc3E6apUP9+spn21iZ2xHkzxts4p91kiJ2q1+yiWrxEbd3ONoj3OpqPDMfCBUVFRUVFZW4dPUAjopEzZjxYMyxpxM0A+zavplVzxdQW3MUnSmJkLcNrTUFx+jfKCruknrfIo+Z2v8uAEnV2e9BY5ISCd7PDhBxN9PqBq01hbSh0zFm5XB81Qz5Ojpfl87eDZ3NwcVTnpMfi/oEd+0xTjIZEIyWuO8tofip0UIk6CW5zx2k9J0oB+wBZyW+gzsQdHrM/aYqSsgPH/qIsrfLFBWLDz7xZyLTRjO0+6Vx72FxeTlLNmyk5osaelzUg4duG09ev36deoMNXJWhZWapj8POCHaTlt/d/3M1AFb5Wmi+6QtQ+WqU7dmKxmCOKdvtbG8EUrBXW32U3MHXMHZkH3KHXIPWZKX+jacRvuT1UqDoJfuXm0i/dSaCIBBqlnYSoxnWqMVSU9lfEPQG0m+dSfbDRaTfOhON0YLrnQ3YDCLllWHFeaLlwq+OM2PWwX1vSNlRuzECkSDe/3yI8eIrqCuaR+WisdRv/gPmS29QGMh3VoX2fnYAQaMhI+9RxXOOEbNp++dbcUucoxnmjNxHQACN0Spff9rN02na+xJ1RfNli6jQJ6UY9R02SXqtwNFmMaYn+EijyC9uNNAzWaDoDgv+uTZa4oiFRXuE1T5hFRUVFRWVs8vAISOloLd8FVWLx0H5qhi7oCiT75mGa+cyfMcOIoZD+I4dlJWVQVqDTb07j7whvZl8x1AW/ulx6DeV7Ic3kT76NwgGM44ua5WM3EfwfnZAcZ6UvhMRA14QBHrOWkuvX5WQNnQ6upTu6MxJijFSbppM2O/GuS1fcV1hbyvOrYsVjzm35ZN01SCcW5dQVXAntWvnIIb8tDbWAxrqSxYqjq8vWYg2OTPu42IwQFLvYaT2v6tjbTV8Fp6PtmE2m0kZPlvxPm23zGTnzi1yxWL0cevQ/8e8l7fG/d0Ul5fz2JpCgv1n0PPhIoL9Z/DYmkKKy8vbe4Ol9dPEa/QceiAJ3xwbLb6IGgSrfG3UjPAFysrlixE1OkVfSPP+QlwfbEEMeDi++gHZ9FyblAZanax63Ly/kLaDO7BdPwrn1nwcI2fH7f2IBoryJJ73KHUbn6RmxRTC7kaMWTmSxdK2fCK+thiJfWO375IccTLlB4aYcpYpxV7mDzZR3RrBZhR4cbRSMKvWLeKr/EQap+k/tDTUQUUpQV84QRa7CkQSlPt4SLnlgfjKz5EItevmgkaHoNFSu26unA12jJiNc8tiNLZ0SXhCEHH5obo1wtXPtUk7knF6gg87I/CvEC+N6cgUJ1LIzrAI+MLiWfmMqKioqKioqHSQyAM43nEgtZJVtZf8RoPmePosbdvyCbubELQ6wu6muGXG8lqlE/7qCnT2boihAMdXPUCwsQrBYEEDhAPKMmjJ+ihMw/Zl1G18EjHoRzCYMfXqTeDEv2Ul6Whrmj6tB55/v0vm+N/FlF673iuRM9GC0ULSVYNIGzqd2nWPdQiS6o2SBVQkhPezAxgc2bIjhzErh7DPTavPjT1eZaK3Le77r6qpiXu/l2zYSNIwZfua7/sDeGLlM4iiyJXPtvHUIBMTr9EDqiq0yplDDYQvUFwuSXY/7G6SJz+NNUUpBrUtn0i7VH+3238vTzDeIwfIyH2UsLuJtlCgo/fDaMZ2fS5iJEzN8/e3P2bh2J9y0Tt6Ysy6GhAIuxsR9Gaa9xdicGQjBgOIQb9i0nNXvIWu8u+8NkEKBq/K0DJ+vYcWvxQUzh8sTWjf+7Mrpn+4cLyZMev9hHpeje2Lv1M4zkS/bJscJLfsf00O1iFa0mMBUYzfV5zWU/IQrjksG8trbRmIkTCZo38d18uvobQAe7+fEHY3kTroPhpKCwgHwKSDuW/5eak9cH9ir59JG7281kn5OtkoBcOdM8BzbjLGbAZM3uTFHxbVjLCKioqKisp5RudguK7mc9k3N54+i2PEbBp3r8CaM4CWA+vkKrTY8mNluXJ0nQYo2rqaS/OxWq2xZdA2B/qUi7h4qlQGHRXfcox8qEPp+Y2naTu4EzHgjfUAHj6L+s1/QAx50RitiAL0nPkqglZag3Wb8BRth96kYfszCHqTQp06ar9kzRmgKBOP9z515qS4j/fs0SPuva75okbRk+2ueAvzPzfx2u0G+mWbKa8Mc/dmL6GISFayRlWFVjljqIHwBYoYlGT3Ba2Opn0vIwgpMUqC0YxmuMvOZLQ3uGblNDJG/wpTr95Ur5jSvkv4Rszk5ywtQJd6MZ4jB8gc/5gio4ookjH6VzhLC+RJz13xFrq3n6XBLyrKWQDmvOljzPd1zH/bz+RNXix6KcPamX7ZWtr8YWyV71I4wRITJI9evxFfr+sUfS9iKEDyjePiZH0XYLz4+wD4Kg+SOVZSbjy++gEcIx7s9P/ZcW0GJDsoaQfUt3MRgijy0uiOwP2qDC2BcIC8tR7cAbAawB8Em0GZKZ54jZ5/1ocZt95Di086LhwGX1ggNS39rHxGVFRUVFRUVL4aiSyUPE0nFPos7oq9tPxtHcGGKo6vfoCgs4r0kbNj1yPFC6Ry5dICwi11UrZVZ4hJVkSFT52b5+N542kwWgm11KKzdyPsbcV2/Sj53NacAQSclR3Z3XYxq9SBP8W5ZXHcrGzE7yb74U1SFeH7JVQuHos+vSem7N54jrxDxNemaDWLXlP68Fk07lpBwFlJ24dbiPg92NO74y1ZQGonVWnXzmXccssoynYuU/Rju3f9maemjY57r1PTHIrAObhvJevH6BXrv5fHmMlb66FnZjdmTZ6glkWrnBHUHuELFLujU89rJCz37nbGmJVD2N0oKzBH0adn0by/kHBLnfyacGs9KX0nAkJsn+3wWfirDsX05mbkPiIH5Kn975b7VkIH1jDlmgjJ7WXDUSZeo+fabhpWfRBk2XATvjk2iu+0MPctP4WfBOXjntjrx26EVj/MLPUpnuuXraXNF6Zu45NULhqLc8tiUgfdB5EQKX0nYr70Buo2zady8Vgad60gqfcw/Mc/pWnfGoU4WKL/d753oeYTmLKlSdmaMwDt9bfTFujo9S38JMicN33MuMFAL7sGQQCdBqb9jx5vQPIVfutoiGBY5K2jIV77JMifbzUBYBr2MJm/3ELmnfMRNMreYRUVFRUVFZWzS+c+36l351G2R9m/2jnz27n/VWdKktdU7oq9NO97mbSh08n+5SbSbp6OxppCsLGGlP5307h7BZWLx9GwfRmCVkfa0OlkTV9N9i83SRv4191KkjZA3bo5tKy+D3fFW0C09NhDRNAotFd0RhO+gzsUfbzuf74JERFdSnfEUABBq6dx9wtokzMSaqN4Pt0vWUqOm0v2w5swX9aHtn++BYJA5vjfIYZiXTQkj+Mq3Ad3kjFmDtkPb8J080wEnR7vzqWKvusZs+Yo+rF1765m6eM/5fYhP475PRSXl+P2+RW9z60trXF1VdwB2LvqZTUIVjljnBcZ4Y8//phFixaxZs2ab/pSLhimTnuQZ55ZRMrw2YTbGhOqAKI1EHI1SHZCRgvWKwdgvqwPrg9KFKU72qQ0jq+aIQe2nTFm5RDxuROoLvvxV1fIWdPG3SsIOet49aDAoO9oGbdeUk++0qFhzPd1vPV5mE1dsrwvjTZzf4mX23J0PLHXz+oPJYXlzj3Df/0owD3XGXjsLR8AVsGH12JHDIcQNFpZtKtz1jeKude11BXNQ9AZ5ffb2Z4pkVWToDfSdmgPns/ewXrNEDwVe2Wf4BNtIj/f5uWBGwy89klQUfI8caOXoAitflHOFPdKEfjDYBPdkwSSDALWnEHyPaxrqD+DnwyVM406P6mcTzSGTu04g7vqyw9SUfkvJVG2FzpKohNZKIW8bbjas50tf4u1ZszIfYS6TfPIHDuXi+79s1wCnTrwp/I4/uoKtGYbpk9ep/DO6HqnjUnFz+IGtNZ0NCZrTFY2bcTDODfPly0ixYiUbNBaparAriXXMf7CpQWk9r87xlLSe+QAWnOHR3GidZHWZJEFvKLXlDJ8NpSv4tUNbyruVbQfuzEEN16l528bX+J/7/0ln1Y30N1uQ6sVqGl0YTNpMV03Dr2jl2wBlWSI1V8prwxjNXztX72KioJvPBBeuXIlxcXFmM3mb/pSLig6964IOiOi34OztABHpzKcus1PozGaFUbo9cULiPi9EA4Q0Rmp3fAEhAJorCk4Rj5E464VcSc/jUnqVQm7m2g5sI5gQzVaaypoDdSunYMupTv2vhNJu3k6LRt+y7EWEUGIUNQloG1NoJ78nyYR4zwXNiNsjlMOnbfWwzs1Xn5xo4HHBxglH943vNT6dDTsWkH60OnUvfEnCPkVglfWnAGy+nXyj++QLZmSf3Sb/AXR+f+dv0TSbnkAnc1BfclCWg+sI9koKTyPXefBbhJo8cPmf4VYnRfb4zxuvUfx3qcUe/lnfZjZO4Jw9XDFvc3q0fNsf1xUviLq/KRyPtEYgiSbmYscppMelyUcRnP0P7S8Xwm6lHN0dSoqFw7x+ny72k0mslDqlnUJk++Zxit/fZ5gQwJrRr9X0l9pqUWbnIEYDKC1piKGQ+3CpiVYRQ+Fd3axVcwLMXr9UtxCEhF//ARE2OchfeRsdDYHztICIp6WmPYu61WDcb1fTLjV2SF+1S6iZc0ZgHPLopiWuej4APY+E2JKu5tL8wn7Yn2MjVk5VNYcPen93rqphGefzGf1CA3VrSbmvtXGSyPN9MtOktZzxcW4vzsYKz5aEAmLAve+4ZX1WMorw9z7hpdki+3UfsEqKqfINx4IZ2dns2zZMh599NFv+lIuOKK7bWV7tpK/+EmsOQMVhuqIITJy5yp3KvMepWH7Mi6eulwO+IiIOEY9hKlXb+z/Gzv5OUsLMPa8mrpN8yWf3i4BY8pNd0kBY/ECIgEvNq2AQSvGDRDHr/fE3eXLydCwbLiJIS974gbKniDsvsvClGIvV2VomXiNntdGw9hiA63eAP6aw2h0Ohyjf6XoYXaWLEKX0h1Bb8H1QQmi3yt/KWht6VKvTms9GnOy3GOjS+lO6oB75Sy3VqcnM0lgyg/0bP5XiIr6CA4LXJwkxIhiRa+31Y/iva/OkzyDg2FIvrwfYjgkKU3uKGDRz+49Wx8Rla+JOj+pnC80huB7PUW+b6380mP1R/9Ny/uVhHQphLt/9xxcnYrKhUWibG9VzefyzyfzHY6uv6benRc3WLY7uuMLhkifME9265D9gq12MsfOpW7dnLjrhzZ/GI3NgNaQGV+IKqU7TW+9iKDVYej+PbxH3u0iVroXd0UZmeMeUwSxQwbezPZdpZh7XYvGqBTi0qdnIYaC8mPR9U9UTLVb1iUYtQLhBCJgGqOFsj1bEypyv7BECoIHXaLj6ufaFFor0Q2AsetLKbpDCo6f2Ovn2b8HuL/Ey9FmkUtSBLwhHb+d8tO446uofFW+8UB42LBhVFdXn/LxGo0Gm+30d4S0Wu1Xet255qtcZ+6YO/n3p/9ky/YSHKMekSe+2rVz4ve+ttQqfHbrNj4lHyeXOO9aQbChCsFgRgx4CemMCFpdzK6jY8Rs6jf/gYjfg87ejYjfTZtfsgOKN8E3++DezV5eGhNrpTToEh3fTRXiBspXOjRyQDmz1MfEa/RSwNnSggi0HdpD5rjHYsqTGnetwHx5H9oO7iQj9xFFkGy9ekiHOfy2fNKG3I9zy2IunrpcVlAEMPvqmHJjbAn0vZu9MaJY0evNThZi3rvLDzoBDOXPU1VTRVaPniz62b3cMWQo4YjSZ/l8RKvRkpx8/v8NnUlOZ376qnMTfLvnp3PNhXCNcHrXWecL8f3sAFclNaI/+jmiePIKhZZPatEnX4Qx6/Kvf50XyN/9hXKdKucH0Wxv5yo3nb0bttQOS57OlXeV1Ufl9VDBknkcPvQRM2bNOWmwvHL5YtmpQpFFSC4AACAASURBVJ/ek/RbHpAUnttLku3pGZRXtsWsH5KMOoSrBuN67w3qixeQkfcoIZeTlv2FhJpPoLVnknTtMFwfbMNXeRBBb1QEpy0H1snngI7y5f27lxHxe2jYvoyIz62oIpRa5rYoquOkDHaA5LQMVr1cTN6Q3qSNeFCurOusJp30g1GKbHpX/v35cfpNSgJiXTUgukYS5Xsxb7BU9fLce2Eggs7s4Ld3TVR7g1XOON94IHy6RCIRXC7Xab/OZrN9pdeda77qdU594FG+d8VVCs87rSl+37A+PUv+WSob9sUcJ4YlgSqtxU7KsJ9jzRnAsQV5CRUI00fMpmV/IYSD2IwQEeMHiDkZGlp8IveXePlPk0hORoeVEsDjA4xM3OilcHxsoAzSZHnYGZHHSzJqcPkiIIqEXM6Yaws2VsORA7LQF3Tu4ZlP6zuvSwF8UPIw0tm70by/EO+RA3JmPZSgBPqlMWYmbPAwqcjLa+OU5TuegEjhJ0H+VhXi1U+CNPvA1l5abREiHFm3Xr7OcCRMa+v5/9lMTradtevMOCujnlu+6twE3/756VxyIVwjnPp1Rsuhs1P1RA59wvGPW09hdDPh5IvgDPy9ns2/+zPJ2brOb8PcpBLL5HumsWzpHwlr9coqt5KFLC+Yz4xZcwApGD586CN2NDXh6BT87ShZCCAf9+qalVRV/UfhN7x4/q8ByP7lJgStDnfFXoVIqa7PXUwqfpbX8kLy+mFScQTND8bRVlGGGPSTdssD1L7+OBqjVenmsTUfxAiZ4+YScjkVAWyicu1a5wmS+9yBu6IMXUp3LFf2V1QR6lIvIuiskj2KtUlpaMUw98+QKqIye3wHbA4i7mYpWdIovS6l/91YruhL1eLXE97v733nYsormxh0iY4rHZqECY/OPD7AyB/L2/hsXfxxi8vLWbJhIzVf1NDjoh48dNt48vr1O9WPgIoKcAEGwiqJ6WoUv7xgPttLFsT0CEcn5KgXnNaeKZdDx/PUjZYYR/uEY8QTkjNpKX8Va84AzIeLeS1PQ3VrJKa/IxrQTt7kxTfHxg+ed7NsuEkxGWYla3D5Rdlm6LupgiJQjk6Wbx0NMXGjl0DKd8mevFjO6goarZzVjgb9iXt4PPT6VQkAvmMHJaspvycme+zd9NuEO5gNXsiwiHL5zpUODU8PkUSxxq7zYNYLCuGvSUVePquq4verV/H7KVPP7AdARUXlW8lFDhMG92eEQ0YAtdxZBVCF/L4OA4eMZNXzSzHd3MV2MvcRSjc+wf79ZbQ21JLZ4zs4nbU4xsyJOW7HZilgHjhkJLlj7pQ3tsr2bGXyHUMBEIwWmvcXktr/rjj+whrqwxby1jbK9oteYxqpjl44hs+SRD41WjR6Y4xolmOkZI8ZfUzQaOUyZsFgii8AarTQ+s7rCCYbhIO0HliPLqU76SMeRGdz0Fyaz4iR43j//Xepq/kcR4qdyfdMk9eV0ey3LqU7aUOnK8b3HTso+wrH42cPzWbK7+eyekSIX/U1xKwPJxX5mPIDZUhSXhnmsosccccrLi/nsTWFJA2bJYudPbZG8jlWg+FvN5FIhN///vd8+umnGAwG5s2bR69eveTni4uL+ctf/oJGo2H8+PFMmjTppOOpgfC3mBmz5lBT/TkH23tidSndSR10nyywEHBW4vqoFI3eRMhVL/XOhoN0u/2J2Oxp0TwiPjf1XQJrZ2kBRMKkj3yI1k2P47CEuHmNFBDmXa6TM7/JRvjFjQZCEen/pvkusu0Cd27wsPY2ZaB4y6Va3jsewRcUaQtA9ySBYFiUBbfq3CIzS31MvV7P6g//Q9ObL5A2VPICbti+DMsVfeVynZT+d9O4a3kCY3uzvCFgzMqR1LcNppjscUOAhAqGFj3UuaFqdhJ6bUc5dDAsIgKvjevSBzNO6hUu2bdbDYRVVFRUVL4SqpDf16e1oRZ7l03ykMuJoDdjunkm9vZ1TjhBm1nY5yF38DV0y7qEqdNm8eO+QxRq1PZOyQSQBKnSRzxIQ2kBlpyBuCvKsF49tL2fd5ai1Nje7yeIQS+Nu19I6NoRbmuU1zDWnAFYruhL5eKxaAxWuaS6c0LDdv0o9Gk9pGRHZz2V4gUQ9HPriDFyhjtK1GKqrr3ScOBNAyl/e0/M+NFy8ESMHJtLaqSBny98gU+rG0i1mhm73kerL0KKWcvVl13Fq/88wpBLOrLj922JMGvyxLjjLdmwkaRhyvJvhs1iyYblaiD8LWf37t0EAgHWrVvHRx99xNNPP83y5cvl5xcsWMCWLVuwWCyMHDmSkSNHYrfbE453XgTCWVlZrF+//ssPVDkpZXu28spfn6eu5nOS07sR8LrxBYKASLc75ysCQcfwWdRueAKNwYxjxIOKCdH9r3JFuUzyj25DDHrJ/uWmdsGHeYh+L4LRjO36XFrfeZ3WA2tJ1oVYmavMAD8x0Mg9m32EIrDqgwAgKNSU79ns5bb1Hpp80m5oKAx/qwrLwfETe/1ydthmhJk3GuTekcJPglj1QWo/2IZQsQPx+0MJNZ+gctFY+dosV/TFWbIoRlHbWVqAGPDScmCdnBkX9CbEQKx9lE4DAsTsYE7c6CUchl72+H3NrgQK2S4/wCl6oKh846jzk4qKyvmGKuT39emqCu2u2Evj7ucRAx4ad6+QnSd0CQSidCndEbR66DeV/KVPM8PnU6hRuyv20nJgHRFPM673S0CjQ2dzyBv0GWN+S+PuFTH9vOnDZ9GwfRna5EwQI+iMCQSqLMk07HhWrtgz9rwawWDBes0Q3If2SFVubY0IeiOC3oTBkS31D3fResnIe5S6TfPZvbuUK6++DpD6omtrjqIxWkj6wSh63rkEf3UFZTuXMWPGwwC83N6K5+jxHe762YP0HjAyrr1bks0MhLj92l7cMnsaRR8dJX/LJl4cZ+oU9B7hphsGMGP3Bxz5wsllFzmYNTlxT3DNFzXxxc6+qPlqHwaVr4QQ8qNzHjun53z//fe56aabALjuuus4dOiQ4vkrrrgCl8uFTqdDFEUEQYg3jMx5EQirfH3ieeK5ShZi+5/htL7zetzdREHQxJTbJF07LKY02LktH21SOoJWR2r/uyRf3k3z0ejNtH28A529G/oTn1A4wRyjlJy31oPNCIO/o6XsWJiiO5TH/HWMlCH9bqogKwPWtYncV+ylskXKLP/8BgPP/iNAi0/qGQEpCJ7zpk8hXjWpaAdtgoAtyYzL5Ub8cAOVB9Yj6A1YI23Urf0tVgOERQ2RUASbSYfLWSWVRW/LR2OyEhGQv3Aadz2H/+NtmHSwaYKFB7d7GbvOgysg+SJPvV7PmoNB6txi3D5huyl+FtlmBEEjPVZcXk5+0Saqa6rUHhcVFRUVlVPiXAmNnmvOpdjd1GmzePpPv8cx6hG5LSxz7BxFZhbA3ndiTAY0mrVt2LZUWkMNncmra1bKatTuir0073tZ6cCxVRLPyhw7h4jf097PWx1f1LT5BFpbOo6RDxF2N8W6eWxZjBgOkTluriLrqzHZ8FSUYbnsx3j+7x90u3O+4ppDrXVxzycGPIS0dpY8/RgGWwoptz5IdqfXGRzZUtvZLdL7/POrOxg4fhJZmdYvvc/Jxha6fbYfd7uI34o3V/PiKI1iLfjiqBA/f/Mj3n7xlS8dT6vRktWjZ9zNgawePc9b0bxvpaCfyYTm8u+f8WEbGxuZOrWjanLChAlMmDABgP/P3pmHR1We/f9z5sy+ZE/YQhBbQKNii7+3SFmVRdkCCVoMCvIKKqhUKdVWlteNKAURkFawirVqCYsESNhkkb3U9tXaqFC1rwgJSCYLSWZfz++PkznJYSYoigJ6PtflpZk558wzc8Znnvu57/v7dbvd2O125TlRFAmHw+j18vepS5cujBkzBovFwuDBg0lKSjrra2mB8PeERJ54mSMfpm7n8laN0aVQfPbT90m8sFTGsOlyCXQTsf7aSNCLoDcRBVwB6ay2R+NKfDT4W8+QuoNy0NslTeBvoSivtAhw79wgn9sy81q0PxAnXrWywEL+ai/r8yX65Dg4cDxCwdoAJn2E4nxReWziRh9zbzCTnaSjcJ2P6pKnsAp+PEEJmwGc656CaBhdJESmXcDpkd/b5w2SyuMYYGBnufzbHZRUmWuLHhr8xAXI40p8uAJw+02DtB4XDQ0NDY3vhG8i5vdd8l2K3V3feyA3Dfwb2zYUIUUiZI35n7jMbN3O5aQNmoIUavYFjglEibZURXzUlJ1LRcVnJKW3IVB5mIZDq0k/I9ObMXwG1evn4lz3BILeqOiYJG7dMhNx1cpJiyYXi1ilnqA3IkUliAZVmetYG5upfTfcH77dVFrd/Hz60Aep3vB0KyKqHUkbNAXn+iJSbn4o4ecQayOrqPgMUQ/XJn2MLape0yVCd/hTTr/7OWF9ChvKP6XCWcWg1+WEwqy+JsUF5OMTzq8keJeU5GB6Qb68XrqpeXPA/dYSnhpfeNGK+30bgn7fVzG/tLQ0SkpKEj5nt9vxeDzK39FoVAmC//3vf7Nnzx527dqF1Wrl4YcfZuvWrQwdOrTV19K1+ozGJYXzxOcJd/lCtZWKMbr/WDlSJCxnQMvmIxgtBCoPq84J1SUWloo0Vit/x0QXBKMVKRzA1vV6kkxytrMlLW2PVhZYcLRyzOWpAv5ZDpYONbPnWETpqzWIgpI1thvh9u4Gbi/xsftouHX5/SCqc9PNUYrzjarHXh1l4XcHg9zQWc/kHgYyDX5Kx1oIzHJQepuVLKMfMRLCZoJJPzWQ3NTTHJWgsjEa95pH6yWqvVDyC6tSvm3QCThMUOWWGL3ai2muizFrvFzfQYfNCI9PmqzqcYnZWdlvepDn3lx3bjdfQ0NDQ0ND45yZ+uAsps/4H6RQoJU1VAWu7UsZOqwAow7ajJ1Lu4nPI9pS5axwLzlLFag8TFaHy5CiEWq2LGpVpDMa9KG32NHrBKpL52Pp0ouaM9Zn1WULcFyXhyG9Iw0HV9Kw4i5qNz2LDT+WH/8MwWihza2PkTNjPWmDplC/7zU8h/c2ZXZ9BKs/J6tgdsLno34PNVsWqV4v9j5iSY7W1pKx95mZ3ZncnDCOE5+h+/TzL/3HHQuC/1PJkjf+SOltVmXNN+ttP8UfhM4qjJWIvD59eGp8IYZ9y6hYWIBh3zKeGl+oJRF+APTo0YN9+/YB8P7779O1a7NNoMPhwGw2YzKZEEWRtLQ0GhvP7rSgZYS/J5zZ6wLNqsmJjNGNOjBcNzKu3CYWHMf1wiS3QYqEqT9YjOu9MrlH2GhBMJgJf/QWSUa+1PbIFYg/ZuJGH/MGmpUgtbGVvlp3EBYdCuILw7StfqyGxGXHZ8rvH61PnKmOWTBt+HeY4jHqzHLxGAs3v+HFFYCVH4RUqs+F63zM3BVg/mBZyfrA8QidUwTMekF5j3U+CXeT0Fdupo7RV+hZ+UGIcdcYePm9EJGmvQCtx0VDQ0NDQ+PCMmDgcN7484uJ1z5muyICdWD/LpzrnkIK+VU6JP5j5bh2LGXqlId47ulHSRv2EKd3/fGsmVf/zqU0nK7B9d4mpICX6g1PEw140Jls2H86nNR+4wl98THm8rVNayY7B467KVz3Dv7utybM2Iq21ISCn6rnDWaQJJW/cUq/Cdhy++M/Vt6qO4ghLVsO0kvnk2wz8eyjv2H7W4eodDrpkNWWGTcNYPT/+2niD1ifQqTt5SyftyCuJHpFnoW7y3xEBFOrwlitkdenjxb4/gAZPHgwBw8e5LbbbkOSJJ5++mnKysrwer1KCfW4ceMwGAzk5OSQn59/1utpgfD3hESm7tVlC7B3H4IUCSPaUhGlMA/OmseAgcPJG9idzN6FGDNymoWx0rKRAr44s/SaTQuJ+N2KCJUU9GFI74ilay9c/9hAkgmKx1ipbIwqKtHtHQILBqttj5JSUoj0/m/yS/9EY309NiO8MKz5GKBVf7nLUwU8IYj4JJYONXPKLTGp1KfqES5c52NyD4Pqc+mckljIKhYwt5ZZNhvADHHl18Vj5El71tt+PqqO8Hp5iEa/xB3d9Rw4HsGgk4PnWB9xyyD4938PApCanApAh3YdEv7gdGjX4Zt+HTQ0NDQ0vudoQn7fjJYCo47UDAJbF5EytNk60rV9KQ/+ajZAkwbLQ6Q0PefcOA93+XYaD61BMJgAiYVFv0VvsROqO0Ha4KkqW8qGg8WE60+hszjwbZ2Hq6ERu1HAExURLA50JhvRgBcpEibqc1H5wkQswbo47ZXiMRYKtuyBfhOU9xHLXFeXLUgo+Bl7vmbLItJvfgBbbn88h/dyeu+rpA2agik7Vw5yN/4OQTRStWoW+pS2JPculF1GyhYQ8dRTu20pqTfcRajuBK+u207GyIeVtq7ZZUuQUtqcNTD99Isa+uTYVY/1yRH57LTEc7+8p1VhLA2Nluh0Op588knVYz/60Y+U/y4sLKSw8KtvqmiB8PeEmM/bG00qfknpbRDDflzvlsoTtdGM2SAHiXt2bUZnslB/sBjfp4cUdWhLl16E/9cZZ5aeesNdSNEIp3e/EicW4bDoKc4XlYl6/LVGdh8NU7DGq7I9GrcxjKHfVGy5N8DVAzEfK8ezfjbZSeoM7ugr9K1mltvaBQrWeBX15icGmJTA224SCLfrzooPPlbJ79d4JQrXBynOR5WFnnuDid1HwzhMrSs+C0Li7PTReomd462MWuVFJ4ArCG98EOKld0OkWgTV2G9704tBFHh6fxCbEQZ1Ftl7XC7T+NUtY1rtcdHQ0NDQ0ND4dkgkMOovm49/51KcTf7BU6c+xICBw5k8IS9Og8Xxk6G4y7erxKiqyxZgaN8Nd/l27N2HkNzndqrWPo7OZCUz7xEirmr0B/7AylF6RbOkcJ0PZ1gg/WZ1EkMQdHiCiTfqG2qdtDSDkV0vLESDAQSDSdlgj6lWh2orZFcMnxvRltqcHImEcG97jqr6WnTWZAS9kYzhzRsB1aXzsZuN2EwGLCObnUdOrriPjDOyzl/FuqhLuwye2NvAhn/L7W2xZEHX9plaEKxxwdAC4e8RAwYOVwLiZUuK2LatFCnkVbK3no/eZuG8/0HQ65Gi0Th1aOf6ImSjIIj4GhGTMgnVVFC3Y5msTphASMK5aiZ9ctQqeH1yRBr8MGptGLfPi80I5sHT5SC4CVN2LlWBKONKo6zMaw5cX34vRMckyFvlxRuSs6pFN8pZ41BEosEPz99sIm+VF08QHGYd6ESs+U9iys6l4eBKRq1ehzsQwW414gqAYdA08jeuoLGhEYfDRgQ7d250YTeJhNtey7jSf6vGcNPrstK1w9h6NjlWrr1rQnPZdP5qr6rM+pRbwmwQVJZLt5f4qPdJDHhoOr+6ZQxPjS9kUcmLVDSpRms9LhoaGhoaGt8uiQRGU0c+Agde5o1d5apjY0rQLYkJi0Y8p/ni1V8Sqq1En9wG39F/YsjoiOvdMqSgD8FgVtw5GlbcxZpR+rgMb36pMU7otGrVLFIysjhw3B23BrGbRPzHylVJifSb7ke0pVK19gmc65/CYdThcnlISknBMOwhREcm1WXzadw8H7+7kawOlzHtoUcZOfo2yjasYvHCJ8k4w0UkM0/+PJwnPielxftvTen6y9q6fnZND1Yc3B4nIDq4d49zuXUaGucVLRC+xGlZ2pPV4TLuuPNeAN7auY3MM2wAbFfdiOu9TYiWJKJBHzqDmarVszGkZ2PO6Y7OZCVj2HTFRsDWZPieMfRBqlbPjpv4Gg+twqyHK//g5mi9pCgAtrUL2I3g8gURTDbc4TA2h1rbLlB5GJ3ZjufyfoxatQV3EJLNcP9/yT7BV7/gZulQc9wPQIoZspN06JOyyJn0ClIkTOPCfGq2LiHS4EQwmNCZU3H0GIj7n5sxpLdFdGTSYEgh67bfqsqQ/cfKqdu5nHDf+xm1ZgnuQJjk9CwsRi96Aer98bZIsex0rFy75Q+aK6jOIBftD/DqKHVZ018KZLuoUL+pzHldzv6++/IfL1qVQw0NDQ0Nje8bzhOfk+aq4eSK+5SquKSet1B34nNAXlu9tGwhLpcLSZKofOFOoqEgggBS0I9gNFN/aA2R+i9UOivOkiLCdSeQQn5EezoRd62ydmqorU5YGtzY0EAKtMjgViIYzUhdBjCutFS1UV9Y4scdMeFe9yRSKIBgNGO/eiC23P5IkTBEAmSZBYrzLU1Z5yDjSpcR7ns/mSMfwVkyl1/NfEZJmgAc+fB9Iv7EIlkVTWvLlm1crSldf1lb198/eE8RQ4Vmt4+pO9/7WvdQQ+N8oAXClzCJSnuWLVuMQUdc2Ur60Aep27EcKeAlHPAiGK1gtAAghUO4P9qNo8cIzJ26y2Uvw6arjN7PnPg8h3djPFWOwyrw0kh1n647KIEEVj2QczX+k58o3nktS39MbX+M7T87sNkENt5mYdDrXsUneFZfU1wP8LgSH/07iYwrjaLvOx6IiTh0JG3wFGq3LlFEH6RImMZDa7D/dDjVZQuIeutbVUK05d5AsKYSoXw7pkEP4Vw1E5BLo70hifzVXhoDcqBeeLWBtnaBwnU+Fgw2qa53Zn9zq8rWAUhtUUp0x7DWZd01NDQ0NDQ0zi+O1Azq979OxrDmUuCaLYtwpGawZ9dmli5+hohoIGvMHEzZudQfLI6roqsuW4CpfbfmrHBNBTpbinLMyZenok9pq6ydktMzE2Z4k5KT43yH6w8W4yrfDt3zKNiyh4ZaJzajDk/UqIwpNmbPkf2YOlyJaEvFbhLiBEBX5oUp2PI61ol/RAr6WLZsMUc+fJ93332HqhNHEQwW1ThjBCoPI5ptXHddT/ZsX6po0Fi69KKmbAG27kPwfXKIUF0FOqOVwgF9z/qZt9Yj/OkX1a2coaHx7aPZJ13CtCztidnvOIZMo6HmVOKgr64CwWgG0YDObCX95mnkzCjBeqXcm9F4aI28O1pTEWf0fqYFU2j/CjKsAq+NVlsdFY+x0NYukG6TS6w5+g6O7oMhGsG59nHqnh+Nc9VMbJKXSOUH+AIhPjstMW2rn/Z2QbFXKrzGQNGNZu4u82Ga62L0ai9Vbomdn0v4rszD2q2vYjMQqq2gdttSJQiGmMWTBff727B1643OZIuzimqphOj+11tYu/SibudyHCbIsslewG3sAuvHWgnMdlDyCyslR8KMWOmlMSAl7G8e12TvFIpIilBXSw4cj+AwyT02Vatnc6K6hnW73z4/XwgNDQ0NDQ2NL0XQiWQMm65aP2UMm46gE3njzy8SNVhUz/s+PaSoMceOzxz5MP7jH1C/7zXSBk3BkN5RdUy4oYrk3oXK2knf83bGbQwra4TdR8MUlvho9Aao3/+G4jssiHpS+43H3n0Irn9uob62GoxWPFiVFrWWY9aZrNTvf0MOigOJnTIaaqsV60t9t368tXMb9JlMzoz1SCGfapyKjVPpfKw/Gcae/XsY0HcAHHiZioUFJFf9L317dMZTvp20wVPImbGezPxZFO//Gz+7515KDxxI+Jl3aZeRcE2UZBIoO7Dv27rVGhpnRQuEL2ESeQeHXTUIpnh/YNkayYoUiSDodMoE7/34IN7De8jKn0XOr2XPOZ0thfqDxUoWGMCW25+UfhOo3baU48/m01hfz2enE0+4R+slXh1lQacDnQ4i767BEPGTpA/hDcmWQpnGAKmmqBJkLh1qRifAbW96lR+JtnYBdxAsFgvGITPo+OsNuAMS7sN7Ob6wAOe6J0kd8N84egwnGvIpIhAxXzxHj5HyD8qNk5XMsNqrbz6h2gqcJU8R9XmwXdGH9pNewBUAi14gxSzEeRoXj7FgEMEflq2gWv6g/flfIbxBibtK5eD9RKPEnRvUx0wq9eEKQNqgKeTMKCFrzBwefunVVn84NDQ0NDQ0NM4vDTWnqNuxnGPz8zi54j7FZ7extgrnic8JN1Sp1let9cVKAa8SwIbq1McY0rPROzJI6TeBmq1LqNm8mOqQhdEb9ZiKXIxaE6CxXU90Rhvh+vgEhiGtA1IkAgiIlmSkgCfhGMINVYTrTwECNiOJg83kZKpL5yMFvLje20RGi/5mQW9Sxlm3cznHFxZQu20pkiSR2m88jiHT2L59E3fceS9v/vVT/v6Pt/isslapPFQ2BvIeoREjc14vTrimmXJLIeNK/HFrovv/y8DyN4u/5p3U0PhmnDUQ9vv9HDhwgF27dlFbWxv3fCAQ4M033/zWBqehZs+uzUyekEfewO6yimFqRlzA23CwGEePkfE7e2XzMbXriqA3qIzjGw6tVu1CxnY5Xe9tijN6F22pRIM+HD+5mSy7jstTE2c8Y2JSniB4ghAIQapFYP1Yi2Ki7gkJTO5hUAWZr462YDUIShZ41Cov3m5DyfzlWmy5N8hlOvY0Otz9Im3GzgVBh96RQdrgKUQ9DdTtkCfwup3LSek3gZTehUTcdRx/Nh/Xe5swZHRSAnnn+rnYu99Ezq/Xk1UwB53VhnN9Ef5j5ThMcLxR4gt34kC/MQBGEfxhibvLfJiLXNxd5iMYlpjW04jNIJCbqaNDksCEaw1M2+rHXORi2lY/464xYDei+rxtQx7kuTfXfbtfnu8h2vykoaFxMaLNTRc3e3ZtRrSlNGUzS0gbNIX6fa9Rf7CYrA6XkdXhMvTJbVTrq5aJgRhygsGMKTsXz+G96ExWji/M5+SK+6jbsZyoz03VqlnU73+DlD63Y+nSk2gkRGOjC50tA68+Bd+n74AoItpSVdf3HN5L/f7XyRozh5wZJaTfPA2dNZn6g8WqY06+PBUkCZ0tmYxhD2EePD0+67zOR6PLQ9TbgCG9o9wi56pRMtlpQ+6jZssiRFsq7SY+T5uxc5GiYXR62WnElJ1L2O9m2bLF7HurFICKYxWtBuX2mxKvaUb26UeVO6paExXdaOax/iY+/aLmm99YDY2vQas9wv/3f//H3XffzenTpwEIh8Pce++9PPDAA8ox2aLX8AAAIABJREFULpeLOXPmcMstt3z7I/2Bk6gfOLB1Ef6y+aSObLY0CtefIiWBP3DU00DEUosU9KKzpSi9IGfb5fQe2UekwYmzZC5S0IfObEMSRHSfH6K4ILGXb0sxqSQTBMJg0hPXs1I8xsK0rX7m3tj8un1yRI41SNiMYOnSE/fxD8jq2gcpEpZ7YTYvBEHg+MICRHsa6ERqtiwiY9h02aR+8JQ4MSzRnkaHKSsUwbCUfhOo3f4CWfmzVPYCUW8DgsFC1donIALJJsiwJlaN7pQsIOrgpZEW1XO7j4aZttXPkZoor44y86u3/LxeHlKpRheu8xFu1zzG2Of9ZWqLGmq0+UlDQ+NiRJubLk6WLSli+/ZNhH1uBKOFrILZcToq1RuKuHvG/wCwdPEzyvoi1hdbXbZA1SNcs2URoKN64zxCzqNkjp6p6h+2dx9CSu9CxYpIEPWYc7rjO/pPpGhYZUdZvfF31GxaSMaIGXKP8P43lMq92Bgz8x7BuX4ulk7XKqKmGcOmU7djuWr949HpyC9dQWN9PQ6HDXfUQdatv1WNLWaHGTtH0InUbltKuP4UhoyOpPQdT+2WxUBTG1l6RxyDplC84vf8dtpI0jMzE/YUG9Kzz7qm6do+k6WDPHFrpy7tMs7vDdfQ+Iq0GgjPnTuXnj178uSTT6LT6VizZg3z58/n6NGjLFiwAJ1Oq6r+Lkkk9Z8ydDr+nUvlvo0mZb/kDFnwwJbbX+mX9R8rx7m+iFBNBaI9nWgkSM3mhWQMn4EhLbH6n2hPI6XvHSrxhkDlYZzr5tLo89Inx4FBlPuA7yr1caxe4vJUgScGyKrR49b5iEahrV3geGPizOqRmqjqsQPHIySbIWqw4T35MVLAh3N9EVLAi2C0gKgna9RvFRGKrOG/IuI53RTwV8T9SFWXzSfqaaDy93dgyx0g2z2tk024q1bPRkzKRAr6yRz1GxoPrYIT5YgieCIQleB4vRTnaTxxg495g8zcsd7X6nuyG+HODX7MIriDkmL1ZDdCCBPJvW5TnfdV1BY11Gjzk4aGxsWINjddfCxbUsRbO7eRMepRTNm5HF+YnzABEA14FTXlIx++z7YtG3A2qTObbElEvW5lTaIz27DlDsDa9ec4S54iq2BOnAVS3c7lpPYbrwSx1Ruextr15/g//5cqCDV36k7mqN9QXTpfuX5sTGeOUQr4cJY8BZKk9AufWZJty70BqVtfGhbm4zOlkzl8StzYnOuePOOc/li79eb4wgLaT3oB/7FyBIMJZ8lcQs6jpPSbIAe4lUcpebMUlydAuMVGQctgv/5gMR3adeDxFS9Ttm8njb4wSRY9I/sNYsothdz1xh95ZUSzEvZdm6I8eEfheb7rGhpfjVYD4fLych577DEMBrk0orCwkK5du3LPPffw8MMP8+yzz35ng9RI7GNnys7FWVul8rzbs2szixfPI6NFQCj3y47A9d4mQMLxk6F4PtyFc91TSCF/qwFk3Y5l2H86XMmc1u5YDjqUHpQbOuspvMZA4TUGZr/tZ+nfg9y5wY/DBK4AdEoROFYvkWyCJ/YG4kzUk0zyTmDLbGkwDL5wCHPna8gc9VslE2z5UU+8n/xV+VGKvX9B1CsBv/vDt5XnBZMFR4+RzbuxZQsI1lYqnn4td3RP71yO1XUcs0lg5RgLlY1Rntgb4LPTEtGoxPj1Pk665J7lhUNkT+OZb/sTZouTTBAMg/mya0m6fiyGfcvYs3iRckzpgQPMeX0J3NS8ueDZvoQntR+Bc0KbnzQ0NC5GtLnp4mP79k1kjHq0hf1Px4QJgDYdOgPyOmrP/j1ktlBnPl02H53FoVo/1G5dghSNIgX9rbpStPw7GvBQv/91pFDi46PeBtKHT6d2y+JWkxSCyUJW/myVpWVrdkaiI7P1qr9QIHFGt0lAtGbLIhAN+I9/gKldV2y5/fEfKyczuzPzfvcHUkY8QsRzWrGu1Ke0JfWGu9A7MqgpW0DbNDs7Dm5n/a1m+uRYmqyftgPw4B33MPXNYj79ooYu7TJ48I5CRvbpd+43VkPjPNBqIJycnMwXX3zBZZddpjx23XXX8cILL3DPPfcwc+ZMpk+f/l2M8QdLS49g0WxLOGlldbgs7viIp57qDU8T9XvkEpd+E7B2603joTVEAl7ZG3j4r5SdUWuXXvIuZNCLIa0j9u434Tm8R/ERlgIePEf2I4h6Mkc+SsRVzbiNf2DlKJQgdkU5uII6suwSxS18d0ev9jLgMpEV/wzFmaj/OFVQSokdRnAHICoacPwsn9R+sj2SuVN3MobPkAPcqIQgyrvpgtEc93noHRmgExFMVqX0OXaNzJEP4yyZG1cOlTFsOr71M0lvsoE65ZZ4bE9AKfd+Ym+AP/xD9g70hCR+udUHgDcoMWGDj9dGq99TMAy+iI6csUVIkTAVX1Riqmz2yLv1Miv6YdezYOcSKpxOsrOyeGxkX0ZeZoXKi9tLTzKaMAUD39LVx5zT0dr8pKHx1TB9w3nl2/3//vzx7Y1Tm5sudcI+tyoYTO41lpqtS8hoUe3m2r6UqVMfAuQKPH23fkq1mc5kIxoK0ObWJ+LKqZ3ri1q1HjKkZ5/xd0fSBk3Bub4ocZBrtFC7ZRE6kw19Wntqty5RVeRVl83H0WNknKVlzNWj5bE1WxYRDQUQ7WmtvJaZ6tL5ZOY9QsRVTejgn2isr8dmhJrNz5I6YBKiLZXabUsJnvoU/7FyTpfNx2y14qz4At3Jp4kGPOhMNtrcVqS6fsbIh/li/Ww2jjWrW+IKzOSt2sY/PniXKbdowa/GxUGrgfCoUaN49NFHuf/++xk4cCBpaWkA9OzZk+eff55f/vKXfPrpp9/ZQH9onNkTXH+wmJqyBapMb8uJu+XxOS12K8053Wk4tJqaTc8imCxI4YjiDQzyzqj/83+qAkcAS6drFR/h6g1PozM7SL95GhFPLeF3/kJtY5DRa8K4/FFsRjDfNAPbWwspLrCqJj5XAD6tkxKaqI9e48cVkEhKTqHRF8DRK4/Gv60lpbc6OxrbvdRZk8nMe4Swq4a6t19W9e/EfiRsV/bH/f7WxDugQV/Cx50BuYS5T47IT1/0sCJPHmvxByFWfhCi5BdWVbB753ofb423csotKWXhNiNEooAEOY/IYhKBysN0vCyH5FtuVL3mxFtuZOIz05o/a4sVn8/7db8q3xkX0zi1+UlD48sxVb6HTgzhyL/pa1/jYvr//mxcLOPU5qaLD73FrgoGbbn9CdYcx1kyF0J+sjpcxtSpDyll0VWVRxFqaxFNVkBAZ3YgRSXCLrWgU0xPJXnQvQmCVrlHWNE42bIIJImq1bNlfZMW/cCx4x3XqavYREe6ktQQjBakUIioz6XYXFaXzSdz5CNYu/WW308soZHekdT+ExFtqTjXF+Hc+DuyRv2m2Z/4vTKkkB+d2SGXdRv8rBljoU+OQ17nlHoJEyXsqiHiqUcK+anZUIQgiPDjvojBPcomQmtl5s5ANGH7mDcEywZ5uOuNPwJowbDGBafVQPiBBx7AYDDw0ksvkZOTQ8+ePZXn+vfvz2uvvcbMmTO/k0H+EDmzJziWIa3d+AwRvydu4k7UQ2zNHRBvAF86n7CrRhGJCtVWIOhNZzwmC2yFamVVwKjfQzTgJeKqRr//96wZbVAmzMJ1Puq8Ery1EE8Q1cQ3bYsPhwmO1CSeEF3+KJKgwysmIQUqSO03Ht+nh1rdKRV0IlWrZ6MzWXH8dHizIFhNBQ6zDskfQfj3WwgGo+oasvriGwgGU8JrO0yQ2SSMdaQmSmVjlKtfcHOsIUrpbdb4AH61lz45IgZRoPAaufwtFJEwzXWhsziUHz73zqXMeHwGJ8SOZ73XRowExeC5f0m+Y77NcV5xjsdr85OGRuuIpz5DH67Hft1luDpc/qVz0Nn4oc9P2tx06TNkyAjeOiOR4CnfztCho5j64Ky443UGM4LBSPrN09RtVLtfQdCJyjpJn9wGwWhWWQ/FHo/63LjeK6Pxb2sQk7KQQkEym4LRQOVhnCVFOEueQgr6EUxWHD1GqCrh7N2H4C5/SyXAVbN5IZ4je8nKn6UEtbHgV9CbSRsyFfvVzRvvUiQsC6Rakqla+zhEQuhsyWTlz1au6d0wJ07MdGVemPyNL+GWTGS1KA+v2bIIz4e7yBj+q4Rl5p7Duwkfep2GWicOEwnbx67M0HFDZz2vjAgz9c1iLRC+xBGiIfSBUxd6GN+IVgPhRx99lDlz5nDfffclfP7aa69l1qxZTJo06Vsb3A+ZRD3BKb0Lcf1tLaUteoLPdrzvk2YDeGhWHawunY/OYMKaOwAkCNVVULt1KYLBqJRHW7r2IlLuov5gMYLRgmhNJrjvj6zJN8SpPxes8VLyCyvTtjb3zRZ/EGLdkTDTfmbkD/8IJpwQ7WY9rkAUKRJCtKcDtFrig6hX7Z7WbF2Csd8Eknvdin7v86wcbWjuNS7x41w/l6z82SplxbCrJi6LXLN5IdGwHp03TP5qLwYdTNvqxxUEqwEqG9WCXn1yRFyBxBO8wwQ+WyrHF+Yjmu1MfnA2bbrezN8/Cp31Xlsseny+sx9zMfBtjvOK9ud2vDY/aWi0jj5cT/J1OdR3uJx/VHfG7fJ97Wv90OcnbW669IkFu9s3PkPY50ZvsXPTkBEJg2AACYnMMxSbM4ZNx7nuSU7vfVW1hnCuL1JKjNtNfF6pxkvqWUDjPzYgOjIQBB0Zo36jul5WwSyc65+RM70Bb1wlnLx+UwtqZQyfQc2mhaoESax6D5raw1qgKsde9yToLXHXdAYiiW0iGxrJuu3pBJ/BUwnLzO25/bEcKWVNno4+OQ6e2BtgXIlP1RIXcxWJvYZmmfQ9wGAm0u5HF3oU34hWA+H//Oc/jBgxgqeffprevXurnnO73cybN49169bRv3//b32QP0SyOlz2pT3BX3Z8qC6xz5sU8mO99ia8h/eQPvRBwq4aTu/5kypzXLt1CfbuQ3C9twkp6CMiGnD5PPTJcaiuF/PVvaGznll9TYqdUtH+AH9pKodu8EvcXuLjLwVqGyHdT28lp/e45p3Ow3sV4auWMv7RoB/RbKdq9WwM6dkk9xpLxtAHqdu5HGuonjWjDXF9KKNXe3GumonDBNEABHYuRt9rPKn9JyrXFoxmiEYx66K4AmA3yX7HLSfuiRt96HXNmd9YwHvmBD+uxIcrAOlDxmLt1puKhQWMGDL8K91rh1mP6+JfZ15U49TmJ43vE3Xh1p+zOyykWt0Y/vMxEUxfei3x1GfYr+0CnTviETvidvlIa/WX/su5mP6/PxsXyzi1ueniZOqDsxIGvi21WLI6XMYdd94rq0S3IjCV0aTUDE0Bbf4sqtY+Rt2O5YTqKjGkZ6t0WZAkwvWn4q4XdtWAFEW0JhORpK+8fou461RrpZgoV8aIGXEb/THLSFN2LlI4AFK8EnVyehYHjrvjNvZtJl3iUvCQP2GZefS9taxs0RN8VaaIJAUV14z2DoEFg82qtZRmmaRxMdCqjv/atWvJz8/n3nvv5fHHH8fnk3eU3377bYYNG8bbb7/N/PnzWb58+Xc22B8Sd9x5L67tS/EfK0eKhPEfK8e1fSl33HnvVz5eMFgSGsBLoQC+Tw6R3tQr3PjOm0rmWBD1igiE75NDinWROftKRS26JbFSF4DCawwU3Whm2lY/h6uby6GXDrOwcIj8uGmui1GrvNQ7Liel3wTl9TKGz6B2+wscm59H/f43iAQ8OHoMJxrwIuhE0m+epjK+D7tqCNVW0NjQqJQyi082cvULbiob5cAWQGgapy1QTXjX80CU9pOXyU8IAkliED1RbE2+wbFeZoMocENnPa+OsjBnt18xph9X4sMdAE9AYvRqL6a5Lkav9lIdtpJ129PU73uN+oPFrW5YaJwftPlJ41KjLqz+x+kPK/8N0KVzasJ/cnPCZEVOEgmbCBnbXNg3ofGlaHPTpUNMW4U+k+k4owT6TOb5pfMVMc6WBCoPIxhMCYNTImEsXXthSM8mVFtJw6HVVG+ch86WQsbwXynlwy05vfsVdGYr6TdPI23IVGq2LFKv34zWhGPQp7Sl4dBq1WOGtGxEWypRvxdnyVMcX5hP3c7lpPSbgC23P4HKw7IAV5OoV0ukLgMoXOdj99Fw8zqnNIrY4xYaDhbHvb5oT6Nm6xLVWCP/3oMn2LzmK/4gxKy3/RSPsVL3iINdE6xEJPioOqK8xl2boky5Jd4to/TAAQY8NJ0uY3/BgIemU3rgwFe8mxoaXw9BkiTpbAf861//YtasWQQCAa688kp27NhBXl4ejz76KCkpKd/VOBXqG30cePezcz7P4XDgcrm+hRGdX1qOM9FOZawnOBEtDeP1FjthnxsxOUuljFizeRHRgBsp5CdnxnoEUc+x+XnkzChBEJt3BKVImOPP5iPa08kY8Sucq2dhECTSrAKTfmpQrJAcRuiSJvCPe5ozxbuPhhmzxsu6X1jjTNPzV3tpCEDOrzfEv97CfHJmrFd6maVoBNFsJ/3maaqdUv+xcmq3LSXirccU8ZFlF3h1lDqLGwhLVEyX+5hve9OLQRQ46ZKwGwUCEQmjCOEImOXNSRr8IAjgn9XsjwzNvb8g20aFIxCKgFEPiAaS8p+I60UO158iOaMtk+9tVqA82z28FL+b55sRN1z1tc67mOanrzs3gfYdOJ9cjGOMBbs/zrYjNNnqWCwWJUgCSLW6STGLGD0Vcecbjn5MJGyi/qNqACJtL2/1tcRTn5FyVSZil46cEDvy949C3ywjfBF+non4tsb5fZib4JvNT98l3+X3bfKEPOgzOW59IfvhGsgYrvbIjQY8KuXolr/58ZaN8zG1v4Lw6ZOEairQ2VJUVXfOdU8qPsBnXsuQ3hF9WnsCJ/4dZ9eU3Od2arcsal4rlS0g6ndDJIxoS0XnSCPiqsXefQi+Tw4RqqtAMFqRAl7Sh0+n4cBf1KJepfOJeutJyciiobaa5PRM9L3GY+3Wl+PP5tPmtiLl2PqtizCJOhpPOxFNViJ+L4LBTKrNik0M8efhYW7orOfqF9wsHWqOW/+NXhPA5Y/QtX1mQtVo2V6yGHsLe0n3W0t4anwheX36KMclJTlobLz456Qz+TbG/aNbz03V/nwT9rtpOPGv837d9B/1/vKDzhNf+vN47bXXcvfddzNr1iwqKiro2bMnc+bMwW63fxfj+0EzYODwswa+LYn53qU3GcbXHyzG9W4ZttwBagsAvwdEI4LRopS3tOZBp09pixQN0/i3NbSxgc0gkGqGl98LUTxGXRacv8rDmlutSh/I7dcYlDLpluXQjUE9+pTMVqwGOioZ4sy8R+QS5oaqhDuw4Xq5Od9ghFdHqYUeXh1l4fYSHz990cPh6iht7AKv56vHm+2Aikao9kpIkhzktrEJCXt/k81gElEC68J1PpweCSJhMpvG5jm8l/p9r6l+YH7/+2eRwiFSRz5Cx6bHli1brNxbjW+ONj9pXCrEguA2Nnl+cTjMuHRyLa/V5AJEbJ7PCYWscec2Z4OrzxoEa1w8aHPTxU8ibRVTdi5RXyMZI2YoayfBYMFx3Ug8H+xSyo9b6o+0DFSNGTnYcvtj736TSqxUFraaixTwyWurM8qvbbn9sXbrzfFn8wnVVRDx1CFJgrIOipVci7ZUBINF1iJxZCJFI7S59QmV+rToSI8XSi1bQKjuRJyolyDqMWTkYBo0hZwzNgQEg4najc8Q9rtp06EzDzzwa34+4hZOlW/kNzPnYxs9qzmwL5vHnaUh/pwXL5Ba/EGIov0BXP4oSRZ9q9ZJz725DvtND6pKz7npQZ57c5kqENbQOJ+0WhoNcOrUKcX3bty4cfzpT3/i1KlTjBgxgr17935XY9T4CrRUjfZ+fBD3PzcjBX24y7dj6dILMSmLzNEzyfn1etrc+hhSOEx12Xz8x8pJ6nlLXFlOzcZ52CQP0cZqDF+8z8oCC0frJf7vtKQoDMbKh1cWWHj784hSJjzuGgNLh1mUMmlzkfy4q/3PEJMzSel7B7VnlNbUbF1Ccq+xyvsxZecSbqhCTMpstURJZ7bhPkOpGuS/T7kllg41k5upiyt3Xllg4aNqCbsRJAmSTBAIQa1XYuLGM0qESnwUXm2g2otyfvEYC3YjmI16ZWwNh1YrpeaxYD5l6HSiBovqMceQabzx5xe//S/EDwBtftL4PmEk8uUHaVwSaHPTpUFMW6UlMf9fW25/2k96gZwZ65FCPlL7jSfiqSOl73jqdi6nbvsyMpoEtVq2lMXKlluKlQqintR+48nKn41gtAAoLhZnvrZgtIBOT9TvIW3Q3SBFaTN2Lu0mPo9oS5Wr5cIhBIOZqK+RrFG/VY0hc+TDROpPxbW7ZY58GNd7mxBtqbSb+Dxtxs5FioZJHfDfmHO6U122QLUmqy5bgO2qG0kf9SjW1LaqarZ5v/sDtsG/VF1f1z6Xem+Yga95sRvhib1yf1qsTHrpUDOB2Q7W32pkyRt/pOzAvrj7ceKLEwkTHye+OHF+briGRgJazQgXFxezcOFC2rZty8qVK7n22msB2LhxIwsWLGDKlCnk5eUxc+ZMkpOTv7MBf5/Zs2szf3n9JaoqPvtKpdAtcZ74nDRXDZXLJxFpcKJPaUvqwHvwfvJXXO+WkVUwm4jnNF+8+kt5h9NoxdT+iiaBhwoQjTjXPYkUDiLoDTh0ARobwGECV0AOLq/M0Kl6f2PElJQBTKLAS+8GGdhZzy25etraBcaV+DgdNtFm1KMcX1igiDw4NzwD0bBiHxA4cUR5LlB5GH1yG6RISLUD23CwWBG6ivrdJLUi0X95qhy0JrJuqmyMkmQSeGmkhWlb/Yy+Qs/z7wS5/2dGFv+tWdyhU4rAc0PMtLUL7DvWvEjtkyPiDoJZH8KzcykMmkaotjJx5rqhKu6xihOff6V7qtE62vykoaFxMaLNTZcOd9x5r1ylNURtk5Taf6JyjNyDK9uPGdKz0TsyaD9J1jNJ9Jsfqq0EziJWGvQR1emQQgGc6+eqyqlrtizCcd1IOZGhExPbMgV8GAx6QgG5nyvRa0T9nsSvHfBSs2khEXcdoi1Vbj+zpeI/Vo69+xDldQzp2XJp9aeHSL/pPhgyjZdfXKy0eaE3kXZls5BW3Y5lJH3xd4pvU1fefeSMsPdYRNUmdzbrpA7tOiSsFuzQrsM53FUNjXOj1UC4qKiIu+++m/vuuw9DU08TgNlsZs6cOQwePJhZs2YxfPhwDmjN7N+YmGiDY8i0r1VG60jNoH7fn8kYPiNuQvf95x05iDyjNySmKGjL7Y//WDnOtY/hMOtw+wJkpgr8fqiJ7CQd40p8/GKtl1l9TUzZ7Gs18Dxyv13u0d3gY8J6HyfdElYDiAI4htyvBLeew3up3flHBJ1I5uhHVaU7UjSK7Yo+1GxZRDQUQPK7SB82nZrNzyFFwkq/TMPBlZjL1zK5R+IS7AWDZXXVKzN0ceN9Ym9AyWofqYnyz/4mnjkQ5LH+Jq7KFJn1tp/S29Q9x/MGmuPeb5VHgpAf367fI+gTexTrk9XiNmdT/tb46mjzk4aGxsWINjddOgwYOJwjH76v2CqJZiuCIMpBYiSs9Prau98EqO0dDWmJW8oMadkqsauWz9cfLI7rFa4uW0DjoTUYMjqS2n+irDb9tzXyuqcpCdBu4vNyu9t7ZRAJEtFb0VmT0bVocWs5BiHB4zErzIi7DsFkJRoJkT54ipwMqa2gXe/nFR9jkHVbGv+2FpCD6KqaU3hS2pI27CH0jgxqtixC0Ily8uLwWxT/wqwKdlcWWMhf7cXVStXep19UI55S96zPGNSH2aWL4eaHmnuEty1mbt5A1bHRRgui9+tbwl0oLtVxf99pNRBet24d3bp1a/XE66+/no0bNzJ//vxvZWA/NFqWNkNTb0RTGe1XCYTDkQgZw2fEeb7VbF2CIa0jDQeLldLd2PPpQx+kesPT1GxaiGhLIUkfIsMs4PHL1/z1dj+Lb7awssDC6NVeftlToGd7Mc46KBYoKkrLo+VM62v58nlhowOzLZ2aLYuI+Nyc3vsqosmqEsGKle44S54iUPEhtqtuxPXuJgSDbFavM1lJGzSleWI/vE0JZq/KFJm21c+RmijJJvCFIDtJrvpvaekUG+9npyVlYr4yQ8cv1sqlPAeORxRp/7tKfRyrl2jvEAhFJNra5X/HeqCfGGDizg1+sgoepnH7UoYOG82e7UtVO8v1WxehC4fwHytXHnNtX8rUqQ+dny/NDxhtftK4VIgJZQktgqKWyP3BYEggkqVx6aHNTZcOZ2qrBCoPc7psPv6dS3HWVpHV4TJuHjSUXXt24u90LdZuvQnWHMe5vggp4KW6bEFcUBv11ONcX4TekSkfF/Qq+iyC0UJWwey4dU/dzuW0n/QCIPfmGtI7Yr/6RgSdqNg96mzJZOXPVolchd11cZZJNVsWYWzXVfE2jvUnu8u3k1UwWzXWwIkjpA2eQtXaxzn58lSlFzm511hEWyqG9Gwgphadig0fdZufIyk5CftVN1O//w2s3Xrj9ofpk2NRfbZ9ckRcwcTJiAPHI3TLTiflqkzVOROvGoItO4m5r71AxYkTdOzQgafuy+fWgderjrNYLJh8l15AeamO+/tOq4Hw2SbyGHa7nSeffPK8DuiHSmuiDV+1jNbbUEdGIs+5Bie2Xr+g8dCaxCU0AQ8Zwx7Cs30xyVa5XLhlgPvAFh/tHDpcAeQ+3wAYdM3/bTPCC8OaveFAngCP1EQZV+IjHAGvy0Vwx/Km4LaMjGHTqVo9u5WyIT+h2gqkIyHSBt/L6d2vyAG0q0Z1fGNDo+JpXHiNgcJrDIQiEuYiF5lWKFzno3iMhVty9XxUbaBgjZfGAOQkC0rQe0NnPdkOgUOVUbqmCcp7SjFD/04ip30RGgMS7iDK+Vdm6Ci6US6XthubNyzePfAyU6c+xBt/fpGKJoXoBx7Ab9P1AAAgAElEQVT4NYDqsalTH9KEss4D2vykcSnx42xZIKlzWvMiyCiGsZp8GD0VGIgkFMkC0H3+sWabdAmhzU2XDokSEKkjH4EDL/PGrnLluCuv/gkvv7iYqppTCAYTIIFokIPekrlIIR+G9I6k3TgZW25/Tu97XQ4885vFpGq2LiHS6GylnLqiRQZ6AWk3TgZkAS0pGqFu53IyRz6iDqDzHpHLnD2NcltbKNA0Ngi4/gU6ozy2oK/VANy5vgj34X2KjVPLYDoaCpB242RFMybT4GNlnoE+OQ4OHA8zrrSUhno/FQsLsJsSB7tXZugSJiMmbY4w+7F7ELt0jLsnt3XpyG1Tbj3rfRMtVkSf9xzv9oXnUh33951vYKqgcT6JiTacWeLyVctoBYM5cYmMwYT7vU2KMEOzVP9uQvtXIEgSoV2LEQWJV0dZ49SX81d7WTrUrOr7OOWWCAWgrV3AamjOvsY4cDyC3Qh1XglLz18gvLeZUG0F4f91IoVlpcTWlKoFo5msgjlEPKdpOLSaqLcBweJQPJEVE3dj4t5ghxFckoVowMfdZT4+Oy1xearAC8MsSrBufqpRCWxtBhjYWeRvJ6JsGGtVvc+cJIElQy1M2OAjGJbYOd6qKr8Ot5d7v2IbFq2pfGuBr4bGDxvBYCDV6sboOYmhSRTLErWia1oUxYJg3ecfX7AxamhcapyrxeSZfNUEROyaL7+4mIaaU+hT2pLcu5C6nS8iBb3kzFiP9+ODNBxaTc2mhehMVjJHz1RX6DVV4CVa9+hMNo4vLEDQG0EQOb3nT4AcCDccLEYK+hIG0BHPaSw//hmBkx/j+H+j8Bzeo7LLjAlrtXa+FPAiGC1kjp4dV03oXF9E7eZF6Mw2rFE3K29Vrw9X5oUpWCvyv39ezeMrXqawZDvFBWbVGmlyD4Oy7lKq9sw6Zi2Yz8/zR/J1JbCMGAmKwa959oXj2xj3Fef1aj9MLmggHI1Gefzxx/n4448xGo3MnTuXTp06XcghXTASiTacSxmtw+FIWCIjSRJZ+bMIu2qU5yOuakK7lpBhkXAJICDhaaWPwxUkru8jb5UXQSdy109F/vD3oJJ9bRlEBkJg+skwknuPo+HQWgSjuVkUq/KwqtdG1SMcCqr6mWMWBbafDKVm6xJlko/qLUzc6FP5B48r8eGXDLSdthbP4d24336OmX2NrPwgpJQ2P7E3QIpFUMZrmuvi7c8juIPyRD2rr4nCawxKOXjeKi82A4SjzRO5wwhuwUb22CJA6/v9vqLNTxrni1Srm6zIScUPGCBisRBpKpOLbSVqmV8Nja/GN9VVgdYTEOhNTJ6QpwTWy5YUse2tUqIBL4b0jli69uL02ysgGgEJKl+YiKA3KOuv4wvzWxWxarmOiWm1pA2eimhLpW7nctIGTaF221JO73uNYM1xxVc4seVkNuG6k9i7D8H13iakoJe6ncsx53THf7ycqK8BwWgFSd9qL7EUaiVIDnqVfmbn6lkJ14eNfnlT7/FJcgZ79JoduPwR7GaRcJtrWFH+bwZ2DivCqZM2R7j/iSI6X5fP3z/6+iXCFoseny/0tc+/UHwb476i/Xm93A+SCxoI79y5k2AwyOrVq3n//feZN28ey5Ytu5BDumDEJu6/vP4SFU2q0edSRnv31BksXfyM4jmnT25DxNuIaLZTtXo2hvRsrD/uSc2mhRh8tWTZ1WXQBau9rZa2tKRPjognJJB0/Rhe/ueblIy18sz+gFJW7DCBNwgR0Ujgva14P3kHwWRVSoTqDxYrfTXJfW5X+l8EkwUpCjqLg4aDxVib/I/DDdVkjZmDuVN3jBk5sqphzXEcRqjwyiXLDX64PFXAF5JwDH4AAFvuDZza8QeWvOPHE4Qxa7w0BMBuhA1j5Z3N4g9CZNkEVRA/qVSenG/J1eMKwK4JViZu9OEPSxyujuIwQWNQJGvsrOZSptL52M1G9uzarGV/v0do85PGN6UuDHaHhRSzhMETaeEHDGabg1DEdYFHqKFxafJNdVWgOQHh69YP3yeHCNXJjhq2q26AK/qwbNlijnz4Pm/t3EZmC8/cmk0LkaJhssb8D6bsXE6+PFWledJq4JrREUuXXkrJsj6lLcl9bke0pVK7dQmWH/0XdTuWE66Xs86u/92ImJSJpWuvhIkDW7feuD/YSeOhNfK1Bt2L3pFBddkC7N2H0G7i87LH74ZnqC6bT+bIR9SJh6Cv1bEKBrNiwZScnsmB4+649WGXrFTl78cnTebxSZMpPXCA595cx4njH2JJTuHOzWFO1DXS7bI2PPz0b2jTfRRGn4+0bxB9OMx6XJdeHHzJjvv7zgUNhN9991369u0LwE9+8hM+/PDDCzmcC86AgcMZOfo2XK6vvjhqWRrkSM1A0EEDEhFPHTqjhYwRzSrSzvVFEPSjN8CroyyqTO/9PzPGiWDFSltacuB4hOT0TJL7TaAeyFu1Fk9QwuGwkT5kCrbcG0hFFnyo27GctMFT5B5fz2nFSw/AWfKUnCGOeeoJOuzXDMDU4Upqyp5VSnxa9hLbcvsTOPERab4KVfBauM5HRb1ESG8iy9EsvuAYfD/B7c+xa0LzexWfbFR2Nov2N6tHxz6HFXmy0Fdbu0CKublE/O4yHxIS13a7hvwBA5n7+kKqTtfJNlU33IXekXHOu9EaFzfa/KQRIyZ49XVol2EGNIEUDY3zyTfVVYFm1ei3dm4jo4XoVe3WJZg7XoVjyDS2b3yGjFGPqkuHR8ygdttS5bFwQ5Uqq5rca2xc5rdm6xJsuQNwl29HkuTjIt4GarcskrPMP/ovfP/3D3WwWzofY5vLcZe/hb37TYrdpWC0YmzbBe9/3lGC8ZZOIDEBrtR+4zF36k7W6EepWvuYEoDrzDZsuQPwHy/H0iVxkC0F/cp70vcaz7jSP7AyL6zq8+2bewXDH5rKp19U4zDrafRF6NA+mwHXXMU/8PPpFzVktstgybg87nzml3hsl/HXT77BTdfQ+Ba4oIGw2+3Gbrcrf4uiSDgcRq9vfVg6nQ6Hw3HOryWK4tc677umtXHu2LaBFS8tpariM9p0vJyeP/s5e3Zvx+Xzk5n3iFIa5NqxlJSMdrhDUpwqs2hJwopH8QVuyWP9TTy9P8i0rX4OV0exW024AyIrymFg5+bJb1xpFH1fOZhN7j2O44fWAgIp9/4FQWy+b6bsXEJ1lSr16phHcErvQhoPrYmzEajdugRThysRTFYymhSu43qJP9pG8Vh18Fo8xsKoVV6MBHCumonNCJLJgf7qobgiBgpLfBQ3BfidUwQl853IY7il0NftTb0tfXJEjtZLSBL86+MPWTf/WRatWYPhtiLMnbrjObxXzlSfPsWS5+ZiNpsZfPPob3zPLzYulXGeL851fvq6c1Ps2pfCZ3spjPN8j9HpD2MxwBWXpZzzuRFBIMnUgMViwRK1ErFYMNvksYk6EUfSuY0zarWg+5Jzoo0WLBYLosWKESMWix6H+ev/1F8K9xwunXFqnB++qa5KjHfffYeMpswnNDtq1GxdQoe7XyTscycsHQ43VCl/n7lOseX2l9WlW4hVSUGfXL4cjdJmzBxV0Gnp0gvfJ4finD0y8x7BuX4uUiiE98g+wg1ViLY0Ir5Ggic/VqrlEo075mccGy+RMFIkTM6v1ytrNc/hvZze9xq23AEtgmwL5pzuhE+fVN6TLfcGPEB+6Qoa6+u5omM6N/a/nl27d/DKSH2TgJa8PqyztWfHwVi/sJ0Dxz3cVbYNa8+rGHb7pHO6Nxoa3wUXNBC22+14PB7l72g0etYgOHbMuWRMYzgcjq913ndNonEm6oUpK51PNOijzS2PqUuDBk+jatWshEbr4YYqXBJ0ShYSlkHnZupYOtTMqLVB3BE9UiRIuM80Crb8hYYaJ0kpKRj6TsKWewPQ5JOb0pZoyEf9wWKVB12sfwWa1as9h/fKWd3Kw+jMNqXsJjb29KEPUrdzOVLQq4z9zF5idyBx8OoOymXMzf3Cbhr/sQazaKA6aCZvlRdPEExis6J0TlLiz8FuhFuu1LN0mEV5rHOKQLVXwhWQ6HHXZCpPfYFhx3LMnbrH7eIuWjQPv9//lTPDl/J38/vMuc5PX3dugkvns70Uxnk+xxjLBP84247b7/9a12hriRDxufH5vER8PqUc2pHkwNV4buP0eX1EvuQc0evD5PMh+rwExSA+X+gbleNdCvccLp1xapwfvqmuSozWMsuRBif1B4vRW+wJA259cnM/f3KvsXEaLd7De7BffSPuj3aTlT8Lc6funFxxn8oGsqVtZCwD6zm8l4ZDqwnVVmJIy0YK+LB06Un49En5xQQBQW9ECngTBuixccfWX7Hx6qxJIAiqtVosYHe9W4YU8tGu048Y0L8nZZvfxtClnyqrLdrS8YtmXpx5L7cOvJ6fT/wNr4zUq5ISk67xs/Tvf6c6KKk0V14ZCfcvekULhDXOC1+m31JeXs68efOQJInMzEwWLFiAyWRq9XoXNBDu0aMHu3fvZtiwYbz//vt07dr1Qg7ngtGyvLlNx8u5ffzdqiAqUS9MZt4jONc9SdhVw8kV98mTZno2ST1vQTCYEW0pBCoPK+rLodpKBKMFBx5u725g4gYfr45W98aOu8ZA4Tof7pABx3+NwPfJIURHJrZJr6A/vJf6fa+RbktXemNjZTiiLRXn+rlYOl2rKukRRD2ew3sRbanoU9rS8NfVSi9MNOBp1UagpRl8LIsc6yW2t6IWbTPGi3rJ/cMhHKaQ0r/sCkBjQCJvlZdwhISCWwJQcKVB8Q2euNFHg18iJOtC0FhbgaVTd/4/e2ceGEV5///X3neySTbhyIHaqjUqKv5ai5xyyhUOD8QKUsUqthYRtbaorYWgBUOg+BWs2HpjOAIkEJCjooZG2yI2ajxosTkQ2Gzu7H3M74/JTjLZ3YgVOXRe/0g2OzNPdtZnns/z+Xze76SBN+HcnC895KL35qv2KSmcmSjzk0KqVgyG/13Xzvnnpnz5Ad8g7oNKab6CQpTo8/Xr2hMmyixr7b1pP7iNa8fmsW/XKlnA7dpegBAK4auu7AgSUwh73ZK/sMpgxnrxNaSOvou2g9ulADfUUi9ptiQPnI4ld5hkG6kymGjevw5P1b6YMmVfzUdobSmAQCTgxTZAXJ8lGnfbe6XYBkyS1mquskJUGh2OCffFrNXclbu4+eab+P2SxyVP85FX5vCrRc8Sbm3CXbwQZ0AgyaRh+nWTuWHkjwnqe/FpXT2DczorptZ9EOTVD4JsmR5fc+XTauf/eqsVFGT0pN8iCAKPPPIIf/zjH+nXrx8bNmzgyJEjnHfeeQnPd1oD4dGjR7N//35uuukmBEFgyZIlp3M4p4UTUT5MtGMpBP00v/1SjFK0Ua8jHPTi3PoEap1B9nvn+kd47mCQ26/QSSrIVr0YID71gRnd8J+SYUuXhBui2VjJSH7T7xFCAXRpWdiHzhJ97sIhhIAX56ZFCEGfrG/WVVaIEAyQMuJ2GrYX0rh7Dfahs2ipKEog0CCW5bi2F+KYMF96yCBEsA2YQKhye0zwOnurF7tBJft8BudoaPFBL6tK1vd8c7EXZ7uA1QD+MCy+xiB9Dhc51CwdZeDWLT5u2+qlukXAogcE0Kph+81dM84f0PKOSpa97npvvkqfksKZiTI/KUBnMHzo86b/8QwpnJvqRfflb4xLNAAO9078IFdQ+C6SyLKwJ7pbLl155VXsLFlKel6nkFTDjpWimGdZIXPnLeSiSy5n+bLHEIJeVHozgt+D2pwsBr4BDxprGhqTJWYtpu9zAdrk3lKAm9FREt28fx0Nu57Gte1JtMm9QavHNmASbe9ti9lYF/1+F5M66i7Z+Ezf+yH1ica9vRDvoQpa39kgrtWGzKShbEVH0O0Vy639XlJ79eWpPy5nzLjx0uejJ8y7//wIT3MDvczI9FhuLd3GxhwHk8dNIdsh+glHExD5b/t5Li+x5sqF/TJO0l1XOJNQqcLo1Ke2Eqcn/ZbPP/8cu93OCy+8wGeffcawYcN6DILhNAfCarX6O28q/2XKh/v2bkdjtMQPGg1mHOPnx/i/+fasYs6d97J82e9wTH5I9vuMGxdxvOhhVv090CVLqiKjo981SrRM2T50ltg70lCL1t4bUNFr+uJYNcTUbEKtTnp1O0+0P1hrc6DSGRDCYo2eMae/pB7dNYuMSoPlB0MIHPu3aBbf3oguLVsKuhsRaPloO3eUevm8WeBcu4pWn8DPf6SXfa7lNWFsBnh1mikmUzylyIMK8W/PSlLz4d3irua6D4I88oZY/mjRq3hlmpHeVhVTijy0xrVX+heh1JyT0qekcOahzE8KUf5XhdPGEBx1+Tg3Vfy5/V+HCGnrAbHf1+s5MREtJQhWUDhx4vkLAzy7uoB2X0Cmq7KzZCmC3yM5bkQ3+TWWFHplnguIAfeenVv44JNPYtYsqePn0/ruxpiSZ8f4+Tg3/R6bLYm290rJmPqwpCniqdonOWlEz6NLzUy4sS4EvHHbyFQabdxxa+296Xv709I5fNWVUh+z2mDBesUEko69x5v7y2NaCh588DG2bSqlrxWenSRfP70wCX7+0iZC2mSOe+DmrSFendyprZJIc+W20hCPPHbbSb7LCmcCglpPyJL15W/8ijQ2NjJnzhzp5+nTpzN9+nSgZ/2WpqYmDh48yCOPPEK/fv246667uOSSSxg4cGDCa53WQFihZ+XDaLbYfPn4uN5ziXpEnA3HGT5yAgVLHoo/qQoC7doUMqaKE3prN887d9WbtPytiGBDLS0VRZguGIjwcZBI0IsQ9MXI8DfsWIk5dzitFevj96y0OnGVFZI65m60NoeYqdYasPYfg6tsBeHWelkWub5kKRFPCzn3b+bIs3eSOrrzAZM6ei7NBgvH39uAIMBxt0AwBC/8K8jIc7WdWeIt3riiYINzNJIt0r07vVK/cF1rhIff8MsyzbeXeHlsuIH2APgW2uLaK9kGDMS1bRmOiZ0Px/+lT0lBQeHbR6oWAtEfPv83Ia1dCmrVSbYv7fdVUFD4asSrsnvqqScRQkEiOhPpeQ/GtJm5thUgBH30mr444XP82PHjMbom6XkPSoFo3LVW0E/Q70Pwd3r1tlQUxRXFati5KrHtUmp2zLmDDXU4Ji6g6c3nZeOuL11KxO+leukkdKmi57G7ah/6jHOlDHbbe9to9XtiPrvijSVseKWILTeaGPWSJ+766dO6Rpb8ZT3Jkx4i5G5gWtlLtDTUYzOo47atJRnULBh/LTdMGYm7+wUVFBKQmppKcXFx3N/1pN9it9vp168f3//+9wEYMmQIH374oRIIn8n0pHzYNVsseeg21KLSiQqEKoM5rkhVRuY5YibZYE5oot51Qu86+bqjvcBdgm7n5nyIRBBCPtRGCxF3izShqvRmhFCA1or1qPTGBNczkzJsNpbcYbir3oRImHC7C++hChAiJA28Ee+hChrKVqBLy8J62VjaDpTir6siZeisGGn/tspdRPyAxoBXa0QXbsHlFiRBrH7JKmZepmPV3wMJJmZ4/G0/zT6YM0DHbVu9uLwCJTeZY8p67ij1cpFDjU6jiin1sRkgcugtRo2ZyPvla79Wn5KCgoKCgoLC1yNelZ193PweA9awu4mkH99Aw9bHCfvcMc/xfXu3c7zuc3LiqUc3H0NjTY279tFYU/EFfWiS0qXfBxtq46tQNx8jaeCNMUmP+tKlWPuPlb1fDI6z0FhSEIIBqXpOk5QuqlJf/1t5YOxuwR8KxGSht24u5pZZt0rnfeIP/0ebP8zgHA0XOeIHthdmpfJJXS3ZWbmi+nTuNSQD7g/3MmPTClkp9c1bQ6iNdqZcecn/fkMVFLrRk35LdnY2breb6upq+vXrxz//+U+uv/76Hs+nBMKnmZ6UD5cv+bWULY6KRjW99WK3SXIZIFoS+euqaN5RyMjho1i9egWCWhejZOgqK0QIeGUTcVfPu5a/yXcrw+4m1B0l2N0nVrR61Hojjo7JtXn/urjlzqmj7pSC4Oa3XpRNxs7ifNwf/lXqB4767QkBr5R5Th78E0ksS5OUjrX/WNoObEOl1eHo6I9p2f8qgX9swKIXqG4RWLY/QDDSqRDdNcv7ix/pWfX3AFumi4Hv4hFGmb9wlME5Gg43CbwyzSh77WNXhBmbvPhDsDz/AXp3GMQrKCgoKCgonD4SVdlFy4fjZlzTstClZhIOxRqG79u7nVUrHkeVILGQ7OhNS4MrZu3jKisEVAh+D+hNuMoKsVw8ApU+/nk0yRl4D1UQbnF2im7pjQgBH+2Vu+RipKXLiLibadyzhtRRP8N84SBqCqaJ67Hx93brMRaFVdU6I2F3EyqNVspC5y9axG0/zcNsENcvdf/9nOS0DMpr2lk4xMDtJV6ey+tcP80o9jJp5I9p5z8dYqwNhCrEjHBScjKNITPTyiy0NNSTnJaOdvBPaCpbeTJvr4JCXP2W0tJSPB4P06dPJz8/nwULFiAIAldccQXDhw/v8XxKIHyaie44rn1mBcddx1DpjJIXYvdscUtFkeSvC51CCvVbltD6zga0yb0wa1QcOPAutjH34HltIRprmiRipUnOwD5kJo27no7vedcx+XYNklsqimL6kNMnPUjj7jUI4aDMqziamZa88wwmUWjK5gA6g3iZt7E5Kcbv2DFuHs7iRR2ZZ1HUQWvvjWPS/dKGgOfjt2TH2YfOwtfvclw7VpIxbR7ezb/BIIDTLcjEsPJHGLk+V8uStwOywDfR7mdfm4oZl+pkr1n10OoTePo3P2X89XmKQbyCgoKCgsIZQEIV6OReMXaMXYWnmt9+SRKz8tdVsWLFE3z84fuUv72XsEaHse8PZMFu8/51tL1XiuD3ojYnY+0/posXrxnLRcNo/9cOVAYT4bYG0ibMp2nvn7ANmBgzhvrSZajUGtH9w5GNIesSPJ/9TRLCat6/TqrCi/b4dq0E9FVXorGmEnTFzzYLoQBp195Dww4xKI2qVR8/dhSzVk2YMDp3Ldn9smnpfRU3l5Twap6ax4YbuKPUy+EmUTjUq01m89vvs/gXtzO/4AnsWh/rJ2s7/IIDzNjsp92vlq4dbDxCZp/Mb/iOK3zXiKff8r3vfU/698CBA9m4ceOJn++kjUzhaxGMQK+b8smetw7TmHtZvXoFV155FW27VuGrrkQIhxKW1ET8Hvo9WELfOatpbazHefwox4sWojKYsFw6kux56+h1Uz4qxIlJCAWpL1kqnddXXYmnah+po+5EpTPgr6vqHFdDXXybo8Y6Qi3HYn5nHzQDIeAFrR5tSl+EcBjX9gKa3nqJcIszrrdxfHEIHyqdASICqKDvnNVSENzTceFWJ87iRbT5xdcselg1zkj40SQ+vNvKjEt1kpBWeU1YOnbhEAOzt3p54/MQwbDAG5+HmLHJizsgxLwWFnQ88ZMZ3DDqqhO7uQoKCgoKCieJSCTCo48+yvTp05k5cybV1dWne0hnDLfceqds3eSrrqR5RyHqoBeNJYXkwT/Bta2Amien0rBzFcmDf4L38AFpw9/z6X4a96wh7Glmx46ttLY04Rg/n1DTF2Kwu2cNNU9Opb1yFxlTH0aXlk163oOkDJ1J3zlP0+/BUjKmLsT334Oo9GZsAyah0pvQ2hxE/B7sg2aIIqR71lBTMI3G3WuIuJtxTLiPnAXFpI66C8+hCqyXjcXYrz8qjZaUoTPJmLoQXVo2qaPn4v7or7K/z7n1CVCp0Np7y9Zv0JnxjopstVQUSa+rdEZULZ+jc9cCsPCXPyX82Vt4L8oj7zUPt2714fSqSRp4I6m/3IIj71c0+SIA9DLDq5NFH+Fo69i6qQZ6a9vxL7RSPL4d4wcbGX7pxaf2C6Cg8BVRMsJnAImUow+Ur2Xu3HslnzytMb6xe9Q4vXn/OjQWO44u5TkNO1aid+RgyR1G2rh5HN/wGGpzUszupaHPBbTsX4cQ9OPcnI9twETsg2agTe4Vv4wnKR3B706oZq02mAm31EMkgH3orTTt/ZM0SXd9f6Lzq/QmhIgAIV9cAYmEx2n1aINeUi0qSQQrnldwm19eNt3bqsITEJha5KEtAEkG8Hao3Ex5zUN7EMw6uPwH/Xn+4UfRHDt8ku6+goKCgoLCidOTj+Z3nXj+wr/4xf3Sa41R26SRYzhw4F2cZSsQBEHy+u2ukVJfspRQm4tgQx19Zs8gZehMvnjubkklOthYJx3bUlEkZnVTswg1H8M2YAL2QTNorVhPw46V0rrFkjtM2tj3VVfSsHOVpNHSUlFExNNM+8Ht0toNOgWyNJYUIkE/rh0rCbfWi/aS4RCOyQ8RdjfJss1i1nobQsDDF8/dTdJV1xNsqMNXXSkKmNpMhA/VEO5o7bqxfz9Ud05m8YvbqRVMaJLtmLtV7KXnPcjDTxXgb29l1EtiNV3UTWNwjobPm4XOwHiakbl7DsDYAaf6a6CgcMIogfAZQE/K0V198qJqiDJj97JC7ENm4quupO1AKRnTHo4rsx8thVGpVZJQVrS0pumtl2iv3CXv7S1dRmvFetTmZFzbCnBMXCDvMw4GiPjcMcIOrg41a8uAibS9tw0EaNzzTEe/i5njry1Ea+9N8qAZaG0Owp5WmWdw9NrGfv3xf/EJghpMFwyUlKm9n1UQbKwFjT5mXPUlSzFE/Oh0ovddtMxZq1ZJ5T12Iwzrp2Hv52EaPAJTijxdbKTEDHK6GfwhuG2Ajhtydcze4uWeq3Q89YGZg7XHKSkvZ+r3+57Cb4iCgoKCgoJITz6aCon9hROJWM6ZlYe/rupLFJ07+4u7Vsrp0rIkn+Du5c6GzIs6khXZJF89naZ9f4nx/q0vWUrKNbclDMKb9v2FlOE/RWNJQaXV4ypbQVqH7gqAEA5R86To/KHSiGuexj1rCLpqUZuTZZosrrJCUIuWSxG/h4ZWDxfOeIiHbr2B60ZfA8CUcZOZMm4ym9xZ/vUAACAASURBVHa/wdzfP9kR5L8h9QKbrVaskXY23GSWaa8A9LaquMjRWWg6OEfDoaOuk3RXFRS+GZRA+CQSz7vuRNSDe1KO7kr3nc6ktF5YtCoay1ZgNFtjRLCgcxcxek4h6I95j/ezilhbgEkPiJYCQgQhEpZ51aUMm43GkoJzcz6W3OEdatZ16NKysOQOp/XvmzvKhhbKHgqWi6+RRL3qS5YCIIQDhD0BnMWLEAI+dI5sUkfMwZI7DF91Jcc3/I72f72OIfMHscH61j907Io6O0zu3ej00B6Q2ybNuFTH9blaDIvb2HSjmalFom1AWIB0s4qnxhnISlJze4mXmy/Vsfa9ILf017JqvAmA56eYmLrei2707RgtaSwvWsXUhfO/9L4qKCgonGxOtBoliIYmjxVo+mYHpHDK6clHMxFqtVrSHzmT0Wg0p3ycc+6cxxN/+B1hd/MJKTrrUjuD4uSB02nY9TQZUxfGrKEadq4iEvRi7T+2U/B0319wbvo9QtDfofrsQ2tz0LhnDeZu6ynrZWNxf7CHpjefJxL0g1qD5eJrJPFRMQNdi0pvksYTzTYfeeaOWP2VDm9jAJVGR/b9m/HVVfHA//0Ro8nETeNHS3/37OvyeGTNS7TsfxXTxyWsz1MzOMfKRf/XHuMvHHXYCEbgiZGd4qLlNWGy05LQ6w2YTGYiJhN6QwRbD9/TE+F0fEdOBmfruL/tKIHwSSKed93q1SuAxLuQUXpSju4Jo9EkBdtTJwyMW3ocldn3VVfi2rESlcEU855gY/ze43B7o/Rz1t3PS7uNIO5CCn4P7g/34JggzxZrLPY4yoUP0LhnDSlDZ0q7rM7NiyEcQmvvTdjTGvP3GbJyIRzEetlY2g9uJ33Kb+TnnPwrGvesIXPBZmqenEKyAYJhMbtrzG+Tlew89qYfmwFGvugho6NsuutuZv4Io2SNtO46E7eVeFnVMY7BORpa/QI5udcghEPUOo+RdlkSwXMv5LDTS3ubilTl/yQFBYVvGM2xw2hDzVgvO7/nN2qDuANhjrp8p2ZgCqeUnnw0ExGJRGhrO/N9q2022ykf548HjWTsyHco21bMF2vnSpv+yQOno7GkoLGmdio6d4iBRoWzzBcOwrXtyYQB9GVX/pgPK3cB4K7ah2PCffLqt3NEkc9wi5NI0B9TYRdubyRp4I3isePmieum6Lm6lEB3V60ONcdquES9jfvOWU1NwTRJQZrRv+TRp59hwuAfy96/6K6Z/Grpcl690SAFvp83CwkdNlLNYptZMCxI9klNIS1FFe8xO28QXrWXgF9F29d02Tgd35GTwdk67m87yvL9JJGoz/flF5750kA4Xk9LPC/anoLtkLedtBF3xKoRliwl4mnpVHI2WmMmzK67iVH8dVVo7b2JBLyAED/AdmSTdNX10u6m1t6blGGzcW0r6DEzHf1ZCHjJuX9zZ8mO3kTqqLskZUONJUU859CZtL6zIf45XbV8sXYuJi1EImAzyIPcmZu9zCnx4g3BeSkqLDp4ZVrsbuY9O3wcvNPCx64Ig3M0VDcL0nXKa8Ikp2VIf3f2Of1ozvw+tW4H/65VgmAFBYVvHkPde6g1QYzXjyfyJe/9xO3gWL0ZvderzE/fQnry0VT437joksvZ/cYuUq+Vt55FfB5SR9+J9ZIRUm9w2N3UkdkVHTlUemMCrRMjmVnnkJl1Djt2bI1pXUuf9ICYodUaUOlNMa4aYuCbj/ezCsKt9dK6yX1wG44pnRlomWNH0IsmKSOh3ZNKb6R5/zpJWwY6WvHqanAf7CyxL/nnQZ5+fSdtvjD37PBJSYVEDhsX9E2n3hNmakmE1pYW0T5p6EzsljSW7VnJ7Mfv+QbumoLC10d5RJ4keurzPRGiPS02m43SLa/x8gvPsHzJr2Ul1j0F21qTFa3NIakRBhvq0Cb3gqCf0r9+IAXR2guH0vrPUjF4DQXE8puLRyS0FPAePkCo+RjOzYuxDZgklTY37FiJfegsNJYU1OZkwi1O+s5ZjUqjpaWiqEdRr86fs6UdSbFkZ5HU19ywcxWRgJeUa24DkPpzwu4mSZBCbUpCbbGTdu09uIt/g0ol7w2+5lwtL00VS3Y+/rmVx970x9gmQac3cHlNWJrkLXqkXc0Zm7xoh9+Jr7qS1l2r+NkvH+Qf9efS3qYsMhUUFL55okFweMJYMchtNn/pMYq3+beXeD6a3yb+1zazr3Wt40fJuO6RmFJi15Z8yQIy2hus0mjlPbqFN+IqK8Qxfr4siBYiEXbsLOG++x9lR0lRQmujXtc9yvHXFiZw0PAQbPRIoqFqg5lIN5tLQBLlAoj42jHmXBpXw8V2ZR7tlbuwXDhIOtZfV0VmnyzCvc8DoLT8LZaXbOSFPB2Dc2yyPuCow0ZXAdIZxT5GDxrKK7t2kb2gGHu36sFap/Nr3ycFhW8KZRl/kjjRPt8vY/fOLQmzvj0F2+PypvN66TIckx6gz+w/ipNe6TKuHT8F6Mw6ryj4Pb2ue7jDiP0uabzuzIvEPuDmY+gc2WIQ/J9/xIg/tFasF8WuBv8EjSUFV1khKcNmy4LfeF599aXLsPYfgxAOdfYMd5mIxZIdn/TvUPMx0YKp4wGUPHA69Vv/gEqnlx42X6ydS9q19xB2N+HuUHiOF+RGVQy3fBLivBRV3N3Mc+0qqUd49lYvkQgYFrdh1YMvoiNUtpKMzHO4e+69DB86GpRMi4KCwilAc+ywmAmeOp5PpCoUJcj9LhPPR/PbQk9roJMRDHcNsm0pDvxhAfu4+QhFDxNqc/HFc3dLPbpJV11P2O+hbdcqGHOPrDc4ir+uCsJB7ENmyvp77UNm0lBWiNqcROHS34HWIDvWXfUmzW+/DIJA4541aKypNO9fh/dQhXQO0/kD0VhSiQS8BBtqcW5ejOD3Jqzi01hTybzrOSlZYf7eDzvGVIvaYCF19FwsucMw9buMhp2rpPVY++srWTRzhnSu5a88zwt5uriVc6vGGWkNqJm6VUtraxvJaekIl05k3wcVZPbJjDuurIyMr33fFBS+KZSl/Enif+3z7c5zz65KmPXtKdieO28hALu2Pk7I247WZOWS3Es4cOBd8kb2l3ZVwz6PaJvUUCsrkRYl+X0kDbxRsgeIUU+c9IDYZ6zV0VC2ApVWj4Ca5rdfFrPGHeXXaqMFjb13l0nbiBDw0fZeKa3vrEeXmo21/xg8VftwZ16EJXeYOIknd5Yfq002hFBI1ofT9MafJa8/EL2EQ20uWspfwaIXP494QW5UxfBjV4TnJxu5vcTLc3lddjM3eWn1C/hC8Pz7QdoDAgKgU0Na5vdY9dzWr/6FUFBQUDhJ2C7/Hm5LNseOKEGwwrebntZAXzcQ7t5e9sXauaSNE6+lsabS/PZLMVldlVbP8CHDOVC+tmPdtJT0SV1Un0uXotIZ0Noc9L39aelavupK0fd31F1S+1h0PRNqc8Vc6/imxXHdOyJ+D72u/63cDsnvianSc5UVIoRDeD7dL9llNu5ZQ9/bnxaz1gXTZFZMoeZj1BZMIyszm0UzZ5A3eLA09rqGVgbnyEWdBudoqKqPkPeaB+Po+VguGYm943dCOETtOxspuOeXPPLSShjbmQRpf30lj4+X9x4rKJxJKIHwSeJE+3y/jOO1hxNmfe/7zeOx9kmb80EFk0ZcitZkZcyYicydtzBuP/GqFY+jNieTOvouaVJ1bs5HCIjWRgjQXrkLU7/LZPYAXccRbq0n68ESUXxr+3JAQJfej0jQFzOB63tfgL1DaRG9GcfEBbIg3tTvMhp3rxEzy9sLsA+9FV91JfWlS4l429BYUggHvJKidHQMUXRpWbTsX4fGZAXvcdwBmFrk4Rc/0vPbYQbKa8LM3uol7wItlzzdjlkHWUlq8kcYuWeHj49dEc61q2j1d/YDtwZUtPkFemWd942WYykoKCgoKCjI6WkN9HWJtpeF3U0cff6XckEptSa2R3f8fFzbCjhw4F0p2aG9cKiYTGisRaXVo9cb8Qf9MQFytH0sKlCVNmE+zW++wPHXFqLSG7FdmSeNI9hQh0pviuve0dVj2FO1L8aNo7ViPTpHtuTm0dUus6tjSPfWtNS0dP6++mmSkmy0tnYKOJWUl2PRx6+cS7LbcftCWGzpss9VLK3OlILp5RtXU3v0CJl9Mlk0cwZTz/nyVg4FhdOFEgifRBJ5130VemWflzDrO3zkBD7+8H0p66vS6FAZLbIA9PXSZQAcOPCubFc17G4iGAoiBDw07lmDMac/vppKMQjWmbBcNIz293dgveQaGvesQaXV96hAXV+yFJVag2PiAuq3LIlVdJ70APVblhB2N6FSqQm1NyYQ0KqlvuQPCEE/DdsLUelNHYrQD+Gvq8K59QkIhwBiyoGSB07HVbqMNIuKOVfp2fJJiI9dEZ76e4AlbwdIMYE/CCWfhXh+som61ojU23LwTouUDfaFwDpgAjmj5+KrrsRYvpa1L5bE3JtT2bekoKCgoKDwXaOnNdDXxXnkv6R2VJGljZtH4+410rWiYlRdMWTlEnY34XQ3yTRaUobOxF31Jk1vPo+9I6tbv/UJSaxKl5aNfegsyQYSnYmmfX+RrdVc2wtpDwVIn/wrDFm51BRMja883XIcIL7HcUeVXt/bn8Zd9aZU7ffFc3djOn9gp2NI6TIsXVrTXGWFaMMBSsrLuWX8OOl6JeXlPPLSOrRX3sCMTRtkwqM3l0TQDfop4bIVtL8em/WNllbnDR4syy4DUPfe1753CgrfFEogfIZx+x33UFj4RNwS6317t7Pv7X2kTf61uEO66icxO4iOSQ+IgbKvnZyOSTVq1N59J9Haf0xnP3FZoZQtBqj+w6QYden60mVE3M3UlyxFCPmJBHwYsnKJ+NxxJ/CIzy0ZxHd94ESJKlaDiozrHpXtpHo+3Y//yMcQCUsPFm1qX9mYmvaswaaHOQN0vPpBUFbufHOxF60KAmGB5yd3Cmhp1SruKPVyuEngvBQVrT4xG2zIzJXG3X3ned/e7Ty7uoB2X4D0vAe/kb4lBQWFbx+NIfi+3YPe7aLt/f+AKv3LD1JQ+A7T0xro65KReQ4N+9dJAWXy1dOlNUWi/l9tci/SrKYYjZamfX9BpdFxvOhhyWoJwH/kE1JHiVV3vupKnFueQKVWxa7VJsyXsr2AJIYV7/pA4iq9FieNu9fE1XSJuJuhfC06wng+fovWdzagS8uSssfLN66WBcLLN27COlb8bOo+3CNXgB4yE40ljcw+Wdx3/XUxWd+Y4FdB4SxBCYTPMEZfOwWfzxe3xHrOrDxZlleIoxxoyMol5G1H3cUvONFOYldfX8f4+TiLF1O9dBK61GxUeiPW/mMkfzuVzoAgiEGjEA5guzIP72cVYj+v0ZIwyO36wOkuoCX2G+tJz3tQNra0cfNwbV+OEAnLgveGHSvR2NI6epE9aIB2YMsnIZ7Lk6tFvzrNRN5rHtwBuYDWjEt1XJ+rxZjfRnsA+tig3gPNb70IiJZN1hQHc2bl4TzyX5LSetHe1koEpGx68sDpYq/NSepbUlBQ+PbRGILvZwtk647j2/w6kbCOcNZ5p3tYCgpnND2tgb4ut9x6JwX5D0nrJkvuMFylT8bVTelqn+RVh0lK6yWtc9xVbyKEQzJP4IYdK0ke/BO8h96F8rXUHvkvaoMJtdZI2N3WY7YXOircuqk815cuQwgFxH7jRIG6vTfuqn1xq/LadhSw9sUS8kb2J3vBi6i6qzkfPSIb05GjR6RgP2X47WICZXp+TOY3btZX4buJSkCtCZ/uUXwtlED4DCRRiXX3HcmeAlBBrZXk/IMNtQl9fd1Vb0p2RCqtnrTx89HaHBx/bSG61ExUIDNz726v1LBjJYbsS+Jmj4WAV/bAAaQHjiY5g5ShsxJ6Dkd87WRc92hMgOzcnI/WZEUX8ZBkFH2Bo96/XRmco8EdAKs+voCWVQ8WnYA7qMKflEXaqLk07FyFJuwFtQ7z4DlS5retdBm2/mNk1lEA5gsHnZS+JQUFhW8XAZOJ79s95Dor8Lx/iIAqXQmCFRROkJPRZpbovGufWSFbN+kc2ZKDhrvqTdnmv0pnJHX0nWhtDppKl+LbUYh93Hxa/lYUdwO/YecqklI7qz6s1iRaXMe+NNsL4hop4KqhfssSIn43aoMFQaXGNmCipPxcX7KU9LwHY4Lvhu2FcddRx1samTTiUjQmK83710kl3S0VRQRdtaj0JnqNu1bK8nZVfY6u2aJuIll9s5TMr0IMgqAlIKSe7mF8LdSnewAKJ05UNTqKJXc49aXL8FVXIoRDUi+IEAyQMfkhUobNFvt9dUbZcdAhtZ+UTvNbL5I66i5yFhSTcd2jtJS/Ivb1Gsy0dJQQeQ9VSCISnk/307hnDaFWJ+6qfZi+90NCjV8QcTfjLF5EzZNTcRYvJuL3otIZuo13GKmj70Jr741aZ8CSO0zyB+4+NiHoT+ipZ/I50Wng1WkmFl1jxNYR7Hal0wsYZm/18sbnIYJhgTc+DzF7q5dgWMwEN5my6HP7aklF0WI2YR8nKlNHPY7TJz2A91CF9HPauHmSXdTJ6FtSUFD49hAwmejdPQjurQTBCgpnAnPuvJe2XaukdZPp/IG4OtZR5gsH4Rg3D43Fju3/TSb7npexXjJC7Aue9CAGjQo61KPjZnibj+EPCzB4DtkLijGOuge1wYzpgoE07FgpW6tFFaG7vuau2of1iglokzKw5A6HUIDWdzYASK4aDTtXUVMwjcY9a7APnYXW5pCSIl2JZot1adk4Jv8ad+UunMWLadjzJ8KeVtTmZDKmPUz2gs0Eh87lkZfWMezSi2l/vXOcGksKBlWE5b+cx74VhUoQrPCtRMkIn0V0t2gyX3A1no/ewLUln7DPg9ZkZeyYiZRtfa1D1VD0ohMCvrglNwgCjji7mo171iD4PYQ6Sq+jvSnRXuO0bplhc+5whI+D9J2zWiwn2vYkQjiM9fJrE+9glon9tckDp8c1otckZyQQ68qmraEGELO+Oo2KX5R5ubnYy6vTOnuEZ2/1oleDWQvtAYE7Sr183ixwrl1Fi0/AH4KMX22TnTvn3Bxqq2vjKlZG1Rc7f649aX1LCgoK3y7OyzChcUaUIFhB4QwjnsPH4FHXcqCjnDkj8xzC7mbaD26ntWI9KoMZUCH43ah0BoLBgKz1LIq/rgqN0SxtpIO4prIOmEh75etY+4/tVJvWmzDm9Md8wdVSxlVtMBPxe/B8/BYaex/aP3oDIeRHY0sn7Gkl4mlBZTAjRHT0mr5YJrplyR3e41orqh9zfMNjaCxJqNRa0q7tZlE1dh5vvrWaRTNnKP2/Ct8plED4LCLeBH7v/Y/KSoj27d3O63t2ShZJkpVRxrkys3dD3wvxHno3Ycm01t4bIRSQZPf9dVVxe42j5cppY+7G8+l+WiqKxIyyzkRrxXrQ6MWJvuW4aDI/dBYaSwra5F7SjmPE78G1rYCwu0l8PRjActGQmJ7i+tKlWPuPRThQg0rVWfL81HgT9+70MnOzly/aBCx6EAT46eU6pl2kY/wrHkIRAUGAeo9Amx9Uag2+6krp3O6dheT/fg6/LVwfPwDvZj2gMZhPWt+SgoKCgoKCwqmhp9Lr1Svz2dnUKLNCcpUVYh/1M7Q2h9g3HPLE9hOXLiPsi9VtsQ+aQWvFetoOlErCn1HXjoayQlQ6E+Mn3yTZXj69Ih+/678yfRRXWSFpE+YTbDxC6z+2SL7EGqMZyxUTSRk6E8+hd+KutaJrF0NWLiq1Csf4+Rwvejju2q/mC3HDf9+KwrifTWn5W6zZuI5DR12c38fBXdfPYNLgoV/3digonFaUQPgMJZFVT/cJfN/e7ZKwU0bmOfh8Xhxd1AnD7ibUOiPeQ++itfcmbfy9aG0OUXgqOYPm/evwHqqQAmTT+QPRJvci7GkFFZK6tGvHSsKtzoTlykBMtti1YyX6jHPxf/FpzA6mEA5S8+RUyU8vZehM6Zy+6kqp7Ce6W6q198Z8/tW0/+t1NEEICUhZ4Otztaz/SENFXYS9s8xSVnjGJi/P/DNIqlkltwEo9lLfHqal+DH8wSCZfTJZMuFqbpwykkZ1Gr99tEDu1dzNeqB5RyH3LnhUCYIVFBQUFBTOcOKtp4C4a6zXXy8hfcrCGD/hxj1r6Hv701guHkF75S6s/cd0yfCaEfwekh29E2ykZxNsqO34rxhsJg+cjvnCQdQUTGXf2/u46JLL+fjD9/GFQmRMfTjh9U39LsO3ZxUvr9/Nvr3bWb16Bb5+l2EfOovmt1+SrbWiXsbRcURbzqLJjXjCW4+8tA4gJgtcWv4WK1/+E3+eqGZwjpXyGje3vfwnACUYVjirUQLhM5Do5GYbc0+PVj3x3tfy2kKSu9kmybKqJUsBSLnmNloPlNBeuSvWIsnbDhHRu1fwe/B+ViGKR+jjlwNpk3vHzRY7xs3DtWMlCALO4kUIQR+6tGxShs+W/PWcm36PfdAM2d8fzUprLClEgl5M519FxrSHAfD+5+/i+bUQCAmSMrTNAFumm2XK0euuMzFtvYd118UqSk8p8gABPiwS+28MHT53k6ZO4j91Yba83Jl1HzvqWg4ceJfagg1kZJ7DL35xvxIEKygoxNAYAiugd9ee7qEoKCgQf520asXjqLQ67OPmx6yx4mV1u7ZHeT+rkKyQohv4vupKnJvzaWtppq1biXL91j+gUotinkI4KCUj6kuW0rj3WVBp8QcCFOQ/JAqdBr3xr+8S/YGjwqb79m6XVQk2HvkvthQHvj2rON4gllpbr5iI+cJB+Koradu9CltKGv66KpIHii4eltxhqA7to6XBidWgwZR7LeYLBrF84+qYQHjNxnX8eaJatpb688QQczeuUwJhhbMaJRA+A+lq3A4d/RtxrHrivU9r79yRjGublPcgDVsfp7FsBeiMZEyT7zymT3oA5+bFqLU2BCGCEA4SbKxF5xDLeeKVKwvhMIK/PaHHXa+b8jle9DA5CzbL5PsNWbkIQX985Wutgcbda4i4Wwg1fiEpHYbbXKhVoNdAsx8ucqiZ8gMtS94OxFWObvER9/U2P4AQ9/O/esQEJo5RAl0Fhe8yjaGvfozVZuKH6Z+j/vww4ZDh5A9KQUHhKxFdJ4XdTRx9/pdiIKk3Yes/Nu4aKyrymag9KtgYXyhL8HvIuCmfUJtLqmRTmWyodQaZBko0S5ue9yCubQWoTVYcExdIv3duzpdd3131Jo17n0VtTpb8ibsnR+JtzEez4LUFG3BknsMDCxdS/dE7vPhyPpGAB7XegvGDDaybZmJwjk2slivZS6jPBTi72SoBHDrqYnCOVfba4BwNh47Wozl2+ITuRbMvjBJ2KJxpKN/I00z3kp05d86LsUkCcaLtbtUT733Jg2ZIogkJDdh9bkr2VjJpxKUJJnQv5otH4P60XNanUl+6TPTx3Zwv+vgmZYAAgq89cbbY3htjv/4JS3E0lpSY4Lphx0rSrv0FGkuKZLcUzWw3vPabmFLn20u82AzxbZLsxviv2wwAqhO+L9GyKQUFhW8/Ufuj8zJMX+k4vbsG9eeHIaQjqO8F1H8zA1RQUIgh3nPbeeS/pLa5aCl/JbZ1y5Ej2QRF11hGa1JcAU/7kJn4qitR6c09rnUArJeMkLLEjvFy8ayoIGmf2X8k3N5Ir5vyZb+3DZgo9R+H2lw0v/0Sar0pVtwqTnKkJz448E92vb6X9I41Xcva2azL61YtlxdiaslfyOyTGXP8+X0clNe4Y9ZSF2alYb84Peb9XdFok2gOQGMwQHtbkFQl8lA4g1C+jqeReCU7hSuekBm3R4ln1RO1U+r6Pq3NgUEVwVm8CJVW3+N5VHpj/GyswYzn07+RPunBuNli68UjMGReRPNbL+LoKAFq3r8u1ku4ZCkp19wGIJXiyB5E2wtJGXE7gCTkpdLqSR1zNxpLCq6yQiwXjyB0wEXauHmE3Q2YDcSUOj+XZ2Lkix5uL/HyXJ48QP7JpTpmb/Xy/GR5j7DbD1f3vyTuffnbX7fz0p++vDRdQUHh20dX+yONM/KVjw+HDB1BsIjm2GFFOVpB4RsmUUuZLcUhWUF2b91q3LMGS+4wsY3s7ZcRBAG9wYS/pVkSntJYUgh722jYXohKZwSNNiZQ7rrWiRJqcyH4E5dZ++uqUOmMCcW1GneLNpUZ1z2aUNyq9sh/ZcF/UlovhEiY1qZ6qTQ6+6bl+Ouq2LIpX9b73NrSEjfD29rczG9nzQagpLyc5Rs3ceToEVKS7dxaEuSFPDrXWNvDPPzbn6E5P7vHe9McgNpgL/5dq1KCYIUzDuUreRqJWwI9+h68u1bQtmuVTLApnlVPdzslf10VTaVLiURAbbRiuXhErG1SyVLmz/81+/ZuByESI7lfX7oMIRAgnGACFwJefDWV+GoqZQ+WaK+Mc3M+gt+DSm9ECIfQ2hyA6CHsP/Jxl9+bZKbtUs9w8SIatheiSUrHcvEIPFX7pJ6Zhmdm4vHDPTt8fOyKcJFDzcIhBq7P1WLRw82X6ritxEt1s8B5KSoeG24gK0nNKx8Emfyah/aOXuJ2P/ROTeEvD/827n0pfvnEStMVFBS+XTSG4Ec5ITLCLjRE8KlzvvpJ9J3/tFxxCe6DHyrBsILCN0yiljLfnlWEGo8lCEhraf/wrzS//ZIssPXtKMSgDtMGOOzJHHc3knN/Z2tX4+411G9ZQsTnFtc6oc61DnTos7z9kqxVLYq/rgq1KUlcCwV9fLF2LvYht8gCcoBQq/NLxa3UBhOFhY+TnvegFPy7ygpJGz9f7EMuXUbg6GeE2xuIdOt9Tk5Lp7ymPSbDm5WWRN7gwZSUl/PIS+uwjp0nndtZ+gS3loY50tzGhVlp3LfwZ1x93WxiC6nlVNVraW/zKkGwwhmJ8rU8jSQqgT7eWE9SajrOTaLAVLKjd1yrnnh2SiaTCXcIHB1lNHpHTke22pJwrwAAIABJREFUVVQ2tFksAKxa8TgaSyqh5mOdwanBhBoVKp0OjdmR0Mc32FALqBLuZFovH4f38D/Q9/qerMTH8+93u5VaL6XprZewD5ohTeBCMCAqKzbW4T1UgX3oLFr+VoS/rgpPWxsZFhWrxhllWd+P6nWEI/DcwSCvTjNR1xrhsTf93LrFh0UPoXBnEbSACo3ByP233JrwvrhOsDRdQUHh24mOsNjjq//y934Z0WBYQUHhmyPResrZcDyhmrPWaKVx12oyrntEFkDbx82H8rW8svGvAEydMFA63l31Jt7//IP0Kb+RlU7Xb/0D6ZN/JVbIvf0yjvHzCbubYirhnMX5qHT6GHskX+1HeA//A8vFI/B+ViH5DXcVt5JV1JUuQ1DrSM+TV+51VZi29h9De+XrpE96kMbda2SfgXbgTG7euopXJ3dmeG/bFuG+W2YDsHzjJqxj5Vl0+6SH4I2VNO8roDnz+/yj/lz+/pH3BO6OUg6tcOaifDVPI/FKm5v3r0NjsWMacy/2LtngRHQXSsgb2R8BpCDVkjsMS+4whHCImienMvjaPJYvfRSVzkT6tffIJmLB287Y8ZMp2/oaxtzh1JculXnpNexYiTl3OKEDLhAi8Xt+kzPw130o9cVEd06FcIiM6x7tVmr9IM7iRbS+s0H0vRsyk4bthaSOvkt23oCrBte2ZVj18cuipxZ5CIahPmRmSpGbNr+Y+dUA7qCGjOmLZOfzVVfGVUWM4ohzX+KVpisoKCgoKCicfuKtp6LP7XjVc227VjHvvodZvuTXX1p2HPK249ycj23ARLyfVcSWWY+fj2vHSqnFC0EQfXujGWSp9cuAyWzGNm5BzPHO4kXYrszDU7VPCni7tpwlD/5JpwhXh+1k6zsbTkDhWgyUk6+WB9MaSxpNISM/LVNR42rBbtLQ4guzZqNon3Tk6JH4CQGnk+C5F1Lnv4D2tiYlwFU461Gf7gEA7N69mwULFpzuYZxybrn1Ttp2rcJXXYkQDuGrrqT94DbJB1il0YriCR1luSdCRuY5aJPFHuOuNO9fh0pvpGzra6DWSruI0Ws4xs9HQODAAdFv2Pffgxj6/gDn5nxqCqbSuHsN5tzhtFe+DqhAq8e59QmOPHMH1UvzOPLMHTg352P+3g9lIl2po+8ie95rUolPV8RSax/9Hiyh7+1Po7U5UJuTcJUuk30m/sqdmFQR2gPxFaBb/eIX2Zw7jJR7t5Fx0xI8pj4ISekIQiTudY/EUUWMMu2W2PvStmuV5D2o8N3huzo3KSgoKJxNxFtPRZ/bw0dOEFvLytdSWzANytdKVXbRALor/roqbCkOMXgePIec+zeTMXUh7ZW7CDbEV40Ot9bT9/an6TV9sZTJBTEZEX09o1cfvK1NCddCXYNslUZLytCZWPuPwVm8mIayQgD0lmSEgA/7oBlSyXT3scdTuLbkDsM+dBaNu9dQ8+RU6jfnc/3gq/n17XfRL9XIphsM+BbaWD3KzcqX/0RKsj3uubMzY4W0FBTOZk57ILx48WIKCgqIRL66KMnZTrzJOZKgN9d5gmW5t9x6J4K3lfouwWTTWy/RXrmLjGmPkHP/ZoRAgv7foJ/jdZ+TPGgGoeZjpE9+iLQxd3eWKn9WQcTdAkIYwduGSqUm7dp7yFlQTNq196DWG3F//DaapPSYCTRqSdAVUSzCID20nMX5qCIhwp5mGrYuoebJqQR3LUel02OZ8CsserGHpSvlNWF6W1WEAPNnO3BXvYEhK5dQ8zFShs2WenS6XzeeKmKUq0ckfmgqfHf4Ls9NCgoKCmcTiYJdgDmz8li+5NcA3Pebx1n7Yon0PE8UQKvUGqnnOJowSJ/0AGqjJf5aRqunZsV0jm98DGM/0Wqy6zldpcu45dY7EwbeGqM5rjWTfdAMhKAXtSmZUFsD99z7a3plnSsrmZZdZ3sBEZ+H6qWTUOlMNO9fJ53LkjuM1NF3oXNkkz51IW9+8BErX3le8gfWaVRcc66Wn1wcIeJrxvnab2h+5hbcH+4VEzU7V/DwLGUdpPDt4rQXNQwYMIBRo0ZRVFR0uodyWuhe2nzH7Clfqyx3+MgJrH1mBcL5QzvLcbr5BevSsuOXNVtTUYUDaG2OjhLnKqm0GqKm8YvJmPowjbvXyEqYjf3645iwgIadqxBCwRhVRUEQ4loSANQUTENtSkKt0+Poojrt3rUSlQaSx4g7pK0RuLnYy6vTOhWgZ2zy4nQLWPXgCwoEygoIukTfY0vuMIRIOEYQrP31lSyaOeMr3ReF7x7f9blJQUFB4Wyi+3M7kZJ09L1d/9tVa2Xu3HtZvuTXJMdJGER8blyly2RrFVdZIalj7pYEqjSWVCxDh+DasZJwixOVzogQETfx45Vp15csxaDR4E9gzaRLyyZ11F04ixdJ443+XbKSaaMFBCHG9hKQtFiiPsaGrFxqjx5BhSBTj173QZBXPwhSfGN0nRVgxqYVBHUpLM4byw0jf0x0a/hvf93OlpcVm0mFs5tTFghv2LCBF154QfbakiVLGD9+PO++++4Jn0etVmOz2b7y9TUazf903KnmjjvnUbB8CYzu0suyexXz733ohMff2nCc7NkzJCXn6j9MkqsFDpweV0065ZrbaNheSP2WfCJ+j9QTE51A60uXYRswCWO//gQb6wi1ufjiubsJNtShS8si6arrCbUcB0EAjQ5n8WKEoBeNLR1UaiJBv2RJoE3uRSTg63x4bM7HMXWhLLD2Xjichor15HSM3ReCwelqphR5aPNDshF+/kM9vx1m4MYNHv763zC+MOjeW48qAM6ihST9eDp2oxZ9+TPUHqklKzObJ382m+uuGSH7zAS9AZPJjB49JpMWm/HU7RGdLd/Ns2WcX5XTPTfB2fPZng3j/Dpj9PpC6PVqTJgJm0wYLSfnb42YTaiT5OfSqDUkJX35+SOtJgx6AxGTCb0hgk17avevz4Z7DmfPOBW+GeJ5CCdSku7uABFv4/vlF56JG5T2yjpXOndN3edo7b1JGTZbShiINpP5GLMvRgX0uimfUJuLlv3rKMh/iGRHb4YPGc6+HQUcb2kUj7/mNrQ2B86Nj8Vos9SXLEUQBNGOKeCTxhsdY0PHGByT7qeloojUUXd102J5AGfxYlrfWY8uLRv70FmSS0dmn0xs6gDlNW2S9kr+236e6+YvvO46E3P3aJjy/66QPovXSzfz4p9WkKTYTCqcYiKRCL/73e/49NNP0ev1LF68mH79+sW875FHHiE5OZn777+/x/OdsifqDTfcwA033PC1zxOJRGhra/vKx9lstv/puFPNyDF5eDwe+e7kXffy40Ejexx/14eApqN0JzoZqrv9bMkdRsBVI7c6EiDYeASNxS7b6awvXUprxXq09t5E3M3YB4mZVI01NcZywFVWiNqUhBAKYLtyEhFvG+6P3yTcWi9ew+cmHAqCICAE/ah1BhrKVqDS6iEk7yF2V72Ju2qfzH7AZoDDzRFsehXpZnh2kjhZ31Pm5Z0jEbZMN8u8glvr/oVrYxXL7v55jDBWa6v8szQE/Hi9HgKaAF5vkLbgSbmdJ8TZ8t08W8b5VTndcxOcPZ/t2TDOrzNGbwgCAR3esIew10swfHL+Vq/HS7jbnJOUZIuZh+Kh8XgxB3R4vV4CfhVt3hNRaT15nA33HM6ecSqcfBJlfj1Nx6SN9Chf5gARXUsdP/I56i35WK/oTAZEbSyjgfOkEZfSd85qSRQren7B76H57ZdJGzePsLuJlvJXZIrPr5cuI+L30OumfFmgrbGmYr5oqJRJVpuTUWn1RNrqadzzDKg7rxMdQ97I/iQPmkFLRRFBVy2Nu9eQfPV0KTAX2968qPVmUkfdhSErVyxz7qiMM5sM3PbsSv48McTgHA0fuyJxtVgOHXXJXvvTH8UgWLGZVDjV7Nmzh0AgQFFREe+//z5PPPEEq1evlr3ntdde47PPPuOHP/zhl57vtJdGK8TyVctyuz8Emvevk5XuGLIvkZQHpfLgf70OqEmbIPrNubYX0P5eKelTH+62m/ggzk2L6DtnNUef/2VnQK3W4IijnOgsXozalETbwTJUak1MiY61/xgpUw1iubVrSz4RjLJgvaWiCEfHQySqdOizZXPYVYuqwwspOlm/8kGQTTeaZTuYr04zMW29B60QTKgOraCgoKCgoHB2kyjzG9j6+FdqNeu6lsrpYlPUWrGeXlnnSkFwNFiOap90P79KbyLULHoXH33+l7Eq05MewFm8iONFD6NLyyJ5oBi4hlqOYx80A++hCiwDb5QpSEfHsm/vdtn60JbiiElKNOxYCYhJD9Fv2ILgd6N7azW1R4+Q2SeTKT+6guUbN3Hk6BFSku3M2OLF2dqGzSBqr3T3Fz6/T6dPMsCx6sNkX6/YTH7XEQD/KZabOnDgAEOGDAHg8ssv58MP5faEBw8e5F//+hfTp0/n8OHDX3o+JRD+FtD9IRANNKOlybq0bCLu5i4G8CaM/fqTMe1h6RyOCQs4/trCBCJavhgvu3BrfQLlQy8RjQ6ECOl5v44t0dmcj6nfZXLbJrUOyw+ulgXrUWVGuf3AEax66GURI+HoZN3si68m3eI7iR+ygoKCgoKCwhlHIg/hkLddtJ/sZpsUFdHqTryA2jHpAShfy9oXSwB5sJza5orRPqkvXSa2j9V9hL+uSuai0XVsQtBHzoLN0jqoad9fUGn10jEIcWyaJj0Qk3FVqTU4xsvHnDZuHo2716CxpODasZKIz01W3yz2rRB1WUrKy3nkpXVYx86TkictlbvIuOkRwm313Lz1/2L8hefdItdV6d3vPMVmUoGIoCIQ1J308zY2NjJnzhzp5+nTpzN9+nQA2tvbsVo7+9o1Gg2hUAitVovT6eSpp57iqaeeYseOHSd0rTMiEL7qqqu46qqrTvcwzlriPQTsg2bQWrGenPs3o9Jo+eK5u6XekeqleaRPfkj2fkNWLiqdMeHuZv3mxUQCXjSWNFzbCqQJO957NQYzoZZjCQJlD85NixBC/k7v4LIV+Os+xNp/jCTwpTZ0lnNHBbt81ZXUb8nnaJsbvQZmb/Xy/GQTdmP8Hcxko/g/qYLC/4oyNykoKCic2STyEO7az9tVCCtRxV2igLprlvPZ1QUEIhopm2v+/lWSWJXaaMGSO5yUEXOoeXIqbbtWSXaWXcfWvH8daoOFmoJp6NKysFw8As/Hb5E65m5cZYVok3vFVZCOl3FtbTgeV9Qr2FBL4541WHKH0/7eNu67/jrp98s3bsI6tjPI9h6qkGw7AdxqNVNLnqO1uZkL+qYz75YZTBo8FI51Ztd+9st7Wbo4/4Q3GRQUvgqpqakUFxfH/Z3VasXtdks/RyIRtB3aGTt37qSpqYmf/exn1NfX4/P5OO+885g2bVrCa50RgbDC1yPRQ0BjNPPF2rmEWo6jsaZKO5e61Ky477fZbHHVENFoSZ/8kKw854orfsgncXZahYCXUNCHxpYe9xra5N6otDr63v40IJZG69KyCDbU0aeLwJe76k1J0CsqNBFqPopOBRod/PxHel54P0jeax7cgVg16ZuLvQRCcFX/y07RXVBQUFBQUFA41cRTYu7ez3siJFpLRbOc+/Zup90XkLlQNOxYKSo3l60ge95ruKve5Iu1cwHQqUHwu2XOFc3719FeuYv0Kb/pXFN19ARbLxmBSq2hce+zkhdx97E4Ms+hMdQ5ZkeCMUeVputLlzJj+BBZi9iRo0dkAX/3rLUl9xqEC4fQWjCN7SvkvZdRxk6ayjGnmy0vn9gmg4LCyWLAgAG88cYbjB8/nvfff58LLrhA+t2sWbOYNWsWAMXFxRw+fLjHIBiUQPhbQbyHQPOOQlQqDWnXdr7m3PqEmNn1e2MCXvGhsYCC/Ido3L2GYKOoBI0gkDH5oZjynC/+9mfmzr03Zqf12dUFtLa2EHa3xPQl13cIRCT9cApCOCQF2inDZtNSURRX0Ov4ht+hNpixXTaGwN/Xk2RU4XQLbPkkxBftAjnJKsxagR9nqrluvYdmH9gM4PHDwP6XUrTkDyckSKOgoKCgoKBw9pHIAumraq14Pe20d7Nb7JrlfPmFZ0jPezCmDLlhp5j5bf/wrzH9usFdq8hMtlJXvBgh4EWlN2G7cpJ8TTVuHvVblgDi2sd84SBqnpwaY/3YtnsVDyxcyPnnpkjjvvu++1iWny9zGqkvXdbRDpfPreOu4dFrhhLueH9p+VskGdXUFkwhOS0D7cCZ6NLiJ0cy+2T2+JldPWICE8coga/CqWX06NHs37+fm266CUEQWLJkCaWlpXg8Hql8+qugBMLfAuI9BAwaFaZuAWzG5IdwbclnwcIn+PjD99m19XFC3na0Jitjxkxk+MgJvPzCMzB4jnRc9dK8+OU5tYfjevb5wwJaayoA5os6vYx1aVlY+4/BXbkb76EKWt9Zj8ZgQqvRobGkkHTV9TG9Nu6qfaiECNZwM60V67EZYGCWmoq6CKvGGaXs7+wtXirqwjT7IMmo5nc/+4VYxqOgoKCgoKDwreeriox2pbPv9150bS6p1DnZ0VsWUCfsRW4+hsaaRuOup8m47tFuNpBD+aJyFxnTHpZlgPWOHJmyc8TfWerpr6tCa+9Nsi6E4d0/Ubu+hux+Ofz+t7dx/ZSLgc+k9154/cX01d5GfuGfqC2qRmu0EPG6ycnK5Ne3zeC60dfgPvghpeVvsfyV56n7/+3deViU9fo/8Pfs8wCDIOByBEo7ZWl51L7HPMc9NVNzSUTEXDhqxy1zC0stKxO0jMSLEj1qhpUJrrlkKR609Fdd3/xqHY/lUu6agojM8gyz/v5ARgaGzYBnhnm/rssrmOWZewa6ee7n8/ncn5uFaBUqQ1o/LSKDDRi18wMoI9qXGxwp6SpN5G3kcjkWLVrkdtsDDzxQ7nFVjQSXYCHcQJT8ESjpZliYnwvRQxt9e5EJaalLIFOqEDZknivpHdyXhkcebV9udLn0+hbjyUPFLfpvXoJSG1Sue+EnGasR0n8WrmcWN+EK6RLv1iHacOLfcFhMsBtvQaYWYDeLCGikRe7WRXDc2U4pb/ti2C0iZE5Aq3AiWJDhs5i72yINyzJhW5kO0R8NLe4QLSjBIpiIiIiqrWyTrKBHn4T5wk/A4bVu5zgVLkNr1ASRk9d5HDgQT7uvvy0ZAS5ev9vDdQxlo2ZuM+WU9iIsmRqP2N6dsfnAd5i3ajMmz0nBpBeTIVNp0FgXgCWTYxHbuzNGtP0zRqx93e11reqmrq93/nAMK3ZtRcYgJbpG63D4oh0TdopIelKLjYPleHbzDxjWvQ8Oleoq/daYeO64QX6BhXAD4qn1f9k2+qqwKDhsVoQ97Xn/t5LOiCWjy7rQcBTsXQ5NmydhPHkQ4aVa+ZfdPL3kaqkqLBJOm9XtD4bx5CEUfPMxmsQsdNso3mh1wKlUI+ypqVDqwnF90wLIFAo00dphtABFdid6bzAhRAt0+lNxJ+iKOkSP7tePRTAReRXjsRNVP4iI7lnJAMCNOzPiRo+bVKPR4eo0yQI8L0PL27sCod2L1yR6mmJcUdMr681LdwvfXctgNxtw8d1ni4vckCAseT4eQ/sPwab9OZi5YhNscqXr/KngyGfI/7/d+GfyvzBvzQ4snjIWMX17Vfj+VuUcwIeDlG4DCOsGC5i+14xjkwKhN9vx5oSJFT6fqCFjIdyAeGr9X7qN/s29KxDSfSzydqdU2o3Q05TnFe8tRviQeR6L55LHllwtbfS3OORn/8ttqnPBN58gfMAs9+2UBs/FzS/T4HTYUPD1BkRO/QgyhRI6pRU3jEDTIFm5BlihWuCRDwz47ZYTOg2gLwIaaQGVDOjYunVdf8RE5CcMP56BTZlbK8eyN2tVK8chInelBwCiKrhIX5WqmmSV+PnEcRSZCmHatAAytRaw26Hr9KxrZLfR3+LKLfGSawI8Nw7VBuFiyrOQawIQ1OEZhHSJL14Slp2Gt19LQEzbh2AFkLw+C3ZVAMLvDF4YTx6C6eRBNHl2ges1Et8vHvCoqBg+dTkXXaOD3G7rGq3Az3kOj3sEE/mT+t0Fmf6wgwf2YOLYwRjcux0mjh2Mgwf2uO67ceV8hVce87NXIaT7WAS26eGa7lxaZfu/9ew9EHaz0XVs48lDuLpuKq5nvoob16+5Yhg9bhL0+9KgCAxF6JMT4LRZcWPrIlx891nX5vJlY7Pdvo7wAbPgMBtxI3MBmmht2BEXAEEJBKqAPh+b0GG1Eb8bnJjQQQW1UoaRj6pwf4gMO+ICUPSqDttGBKBxoAwLV3vubkhEdC/szVrVyj8iqhulBwBkCiW097WD7s5F+uoqOXcxX/gJTrsN5gs/Qb8vDaPHTQJQfN41YtDf8WX2lwgfugDRL21Hk2GvQa4NhPH/druepwgMhcJuReGed3BpRTyub1oArUaLW7veKXfsGbNfRdMWLRExdAFCu49xxR7YZzoWLd/oiu3y5Uuw3b7uOn+6/W0mVE1aIndHcnFDrR3JsIe1xKvpGyp8f60jI3D4ot3ttsMX7WgZIsP4PQ5MHs61wOS/OCLsQ6q68lnRVU25NgCN+0yGJrINzBd+gtwqomDvcoT0v3vVsqr930qObTfeQsHXGxBWyRTpTzJWI//OFCWzWYS2z3TkZ6+qoMV/5J3N5UWorv2IG0XAs5kmt9fOMzkwZY+IwiKgVagMn/7Hig8HC27TfDYOEzC0zPOIiO7JubNSR0BE1VDdac2VqazrdMl5l9lmR5Nny++gkbcjCeK+VNzQ6+G0miHoGsEhV6FJzN1uzwV7lxc/5lYegsOaQgk73kueByg1aKzPKx971iXX95GRUbimt7jOn6x5l2AX9W7bL+XuWgbRWICt+3M8jgrP+Mc4jE9bgQ8HwTXDLn6rCItTgyXTZ6DP43+t9mdF1NCwEPYhnqY+l56eXNFeek/3G4yjh9e6Evz0mfMq7Bpdouyam8cffwIH96XB4gDC+s8oF8Pa1amuKdVlj5OengqhdXfXvsCl998L6T4Wl1Ji0DRQhs9i3KdBj3xUhdZhcryaU+R2X+8NJo/rhPVF9fBDICK/YFOGSB0CEVWhutOaq1JR1+mS8y7TpgUeZ7XZzSbYAoLRJOY1aCLb4OraKeV6sIT0nwUcXouu3Xrjq+wvET4oESEla4y/WA6ZXOHWOCvqvijXa8z/xwjMTPkX8vakIPDRPpCpBUSUab4VMSgRN7YtRvL6LI+F8LC+vfD/cg5jWNZ3KCxy4pFwOSZ2VOHT/9boIyJqkFgI+5CqrnxWdy+9gwf24OA3Bz12jS59BbT0yPPBfWno2a0nvvh8k8c/Btfzfi/XRbp0TO+98wacVjNubE+Cs8hUvDVA1+dg+uVrBKns+CwmoNwI7/S9ZgDAR0PcR39bhcpw+KLddRtQPM1Hp/kjny4RERH5kooGACqb4VYTJeddcm2gx4JbphbcBihKT2MuoYlsg4tXzuHLvOuIGLrAfVR5wCzk7U5BQOsurjXCSQsTXM8tKWxfSlkFw49fwWkRPR7faRFx+fIlVOT7079g2wjB7bypd0sbpn36EUeEya+xEPYh1bnyWZ299DyNLIutu2PFe4vxXvI8KLSBCGg/oNyo79HDa9E0sqXnxg8hzdwaZ5VWen9iu/EWbn29AbaC35H/ZRqaaG24YfHcCfpkrsP1dWmv99AgfqtYbgT5L60fq8anSERERA1BdQcAqsNT9+mS867ANj2Ru2sZIkrttZu3a1m5wtRT5+iS5lg2s8HzqLIhHxfffRbBEc3xdlIiRvRvB/uZu0VtTN9eSF6fBUuXScjdkey5INcEILJJxU2vzty4ha7ROrfbukYrcOrKjRp/TkQNCZtl+ZCqGjoAlTfTKlG2qZbx5CEYTxaPEEfN2YawIfNgPHkQxpOHXI/RRLbBjSvnMXrcJOTudG/8cHPvCjTqEo8blazJGT1uEm7tege3Dn2E8P4zEP3SdgTrAjGxowo6DTw2cghUF3eEfvOQ+5znyGA5bpudGJppgmaxHkMzTcg1K/Fsz941/UiJiIh80v79+zFnzhypw5Bcz94DsXbDTuw88BPWbth5z0Vwenoq0HUiouZsA7pORHp6Kh5//Ano96Uh4KG/I+DBv+HG9iRcfPdZ5O1IQr8+T7sGB0qUdI4ue55mEw1QNY7y2KhUGdIMypBmMBn0eGHKS3i0xwRsPvCd2+MuXy7ehqmkIC99/Nxdy6CAE/P/MaLC9/dgk1CP51mtWzSp8WcllZLz216dWlZ4fktUUxwR9iFVXfms7jYCZUeWb3+bifAy6349bfjepMX96Nl7INakp+Dml2mw3b4OVVgkQrqPhSIwtNI1OT17D8Ta1anQ9rk7El1YUICN/5Fheic1JuwUsW7w3RHehM9FrBygRWSwHKO2iQCKR4JLmjw4FSqon3oR0W2Kpw0JF37Ce1vSuQE8ERE1eIsXL8bhw4fxyCOPSB1Kg1BRD5ajh9diypSZ+CRjNYwe9ik+eGAPli9fgojBxc2xFIGhcFotyPsiFfbCXDSNbOl6vhj1V+TufMf12JJeKY26PoebX6QCcCJ6znYUXT6J6SuWYt6aHcjPy0VkZBQah0eg6PJJNO47GQCQuyMZDrMRMrUWASol3n15WqV7CU/t9zTG796OD5+xuc6zxu924JXnE+r6o60VtbFNFpEnLIR9TGVTn6tqplWi7Joa682qN3wvvebm+SlzkL4qFWFxi2u0Jqfw5nU0KvU6QRoZ1t3p/tw2QoHxO0VcKHAiUA2EaGRQymVuHaGTv7EgMiwYN4xA1EubIVPc/fXVRLbBpWtXavZhEhER+aCOHTuiT58+yMzMlDqUBqGiHiwXL5/De8nz0KTF/Zg9f4nHPihlBwca9/knFIGhwOG1WLthp+uxJYWbp4EEmVINmUIJ06kjAACbQgNl7xmuos+w623IvlwOPD0LoU9ORMBDf4dx3wose2FMJMf2AAAerElEQVRspQVwicH/0wGOkAhM2fIZzlzLw4PNwzFjdDyG9XoShYX6P/rx1bnqnt8S1RQL4QakutsIlB1ZVmqDKlzTcillWLmR5569B0Kr1WLt6hU1WpNTeiQ6f/9KGIqcbut/ZQAOjA1wXa2csLN4JHh4GyX0RcCZrC3Frz9zlsd4WzRvUbMPjIiIyItt3rwZGRkZbrclJydjwIAB+P7776t9HLlcDp1OV/UDJaZQKCSJs2lUqwr7n/xpYnrxCOSqVGi1WvR9eqjbc6fPWoDlqUvdBwf2p2HWzFdc72XQ0JHQarV4P3UJ9KKIpqUem/fFcjR+aiqUunDc3LsCTgDhA2a5d54e9DLs2cuh/fZfuHjxIqKjo7E0cRJGDugLANj0xX68sWqD6743Jo913QcAjgABzw0YiOcGuJ+nKeQKBAdX7/N2FAoQBAE2tRqCXIAgmKHT1k8ZUdn5rS/8XgPS/W5T5VgINyA12Uag9Miya21Mma6LM2a/WmFx2/fpoejcpWZrckePm4SVd15HPPYFgu+sDe7VUomkb4pco8NAcXfodYOLO0c3C5IhSH33OLOHx+C1j1cA/e5uxWT4agXeGsNN4Yno3uTbgCCdgEDxnNShELnExsYiNjb2Dx/H4XBAr/f+kT+dTidJnM+Neb7ceVDeF8sR2iMBMoWy+Lyq73SsXb2i3LlP5y69MQtwHxyYPBOdu/R2ey+du/RG5y69cfDAHqxdnYrreb9DHtAIMpUGN79IhSosEgFteqLw2yyPs/Qu3byJk9vdL4roC/XYuj8Hie9vQOBTMxAV2wbmyycxfdkKmEXRNVosmkTYPYz8Bgfrqj0irDCJ0IgiHBYLxCIRoihCb63WU/+wys5vfeH3GpDud7su2Z1O3DTbq35gDT1Q60esGAvhBuRetxGoza6LgHvnxYjIloif8AK69xuMvz8zHEEhTqQseguCCnih1Nrgn/McHjtH/5znQMLnIqyl/j8rWQf83pZ0XLp2BS2at8BbY+K5PpiIqqWk6C0tCMBfI85Bd+U33D56EfbIjtIER0TV5qnT872cu5Q9D4JSg8ZPTXH1SQE8z7Ar+/qepk9X9NjbAGQqDcL7F1/Uv31kIxzHtkAGJ26vTYCq2wQE3umDUnT5JCIjozweN3l9FgKfcu/zYm7dA/OWLce0t95F68gITO7VGwOfaVXjz8Vb1PU2WXSPnIDCXvuFcH1iIdyA/JGCtjrbLlWmJMFfv3IOck0Agjo8g6iR76Ho8kmsS3sbbRrnAQA+WPIB7KIRZltx86u2EQpM32tGgAoe9wZupAEW99Jg3A6z2+sN7tqVhS8R1Vi+DfhzlBNRqnMIVLtffFOdO4vbRy+iiEUwkderywZKcoUct3I+dI3UNvpbXLmmoDV5fU+Ple96B4FtekJ7XzsYT+ZA+HknNo7Q3FkeZsGoz9NgdDig0EXAuG8F3nhhrMdYL1++5DZt2HWsWPWdYxkwftdWOEIiMKhr9z/0uUiltgdsiEqwEG5gyk55/iRjtavRw71eKa1K6QQfXaoTojo8uvhqat8XMf+Nd1EEJewmEQo50CpUVtwB+jEV4h9T4bP/WJHwuYiPhghua4Q/GCCgWZAMYYHqqgMhIqrE3SL4OkKunIXdpnHdZ/jxDACwCCaf8cQTT+CJJ56QOgzJ1GYDJU+Fat4XyxE2YCaUunDkfbEcCrsV02fO8/j6xpOHcPvbTFhv/Y4V7y12e8yNK+eh0AYibMg8t1gjBs1F7o5kFH63GTqNHFkj1G7LwzYOAQZvWo7QpvfhjUqaYkVGRrlNG7Z9+zGyBsvdjvXhIGDKls98thAG7p7fNsQpxiQd7iPcQFW0J15d7LtW+o9ByVqasP4zcPvb4m6Wmsg2yNebEPjUTCgcRTBaikeDJ+wUkXPOBqvdiWZBMhSanRi5RYRmsR7P7xLxZk8NmgXJEL9VxIIJk2s9biLyL0E6Aa2aCAi5chY3fyxEwX9zXf9syhAWwUQ+5MaV8x7X0t4oM325Ojydx4QPmIXC77e4vhYEwa3ALnl948lDKPh6Axr3mYzoOdsRNmQe3n//XaSlLnGdg9nMBo+xOoqMiJ6zDYYim8flYSarDEc3plfaGXr+P0bAuG+Fa2/h2zdveDzWmWt5Nf5ciBo6FsINlKekrrtzpbS2VfTHyHrzMoDitS1Oa1HxHwwLEKgGIoPlSHpSi+l7zdAm6TEsy4RpndS4nqjDp8MEaJUyJHxuxtBME/JNTp++iklE3snerJXbPyLyHSUNlEqrqEFoVao6j9FEtkHhzeseX//2t5kI6z/D7XwrpP8sOFSC6zZVWJTHWFVhUZAplGgU1gSHL7qvtTx80Y7WkRFVxh7TtxeWvTAW6iOrcSllGBpplR6P9WDz8Gp/HkT+goVwA1WbV0qrUtEfI1XjSJgv/ATDVyvQOKx4M3itUganE0j4XESzIBmOTQpE9pgA3DYXjxIDQPxjKpyYGgTzAh30RYCSv6VERERUyuhxk6Dfl+YaCTVf+An6fWkYPW5SjY9V4XlMWKTr67IFdsnrW29e8ni+Zbt9HcaTh3B13VRYb17Cje1JuPX1x65Y8/auQKO/xQEAlH8bg1E7Ha5ZcjnnbBi/x4EZ/xhXrfhj+vbC0Y3puP71bixNnIXxe8oca5cNk4dzZw2isrhGuIGqyVZKf5Snbn55u5fBbiiAKifNta3RvHVLESLIMbG9Aqt+sOD5XSLOFTjRMkQGncZzsyydBiiyKSp6aSIiIvJDtdlAyeN5zBfLEdJtjKvALtuhuOR1Vry32OP5llwIRsHXGxDW/+5Wj7m73kHht1lQCkEIbD/A1ZU6sE0vFORdwrAt21BotqN1i8Z4Zdp4DKtkSnRFSp4zbX0GTl3ORevICMwa1BsDObOOqBwWwg1UfbaaL/vHqNl9rbAyeSpGtP0zCv6b65pyuPzTD5ExUINeLZVoG6HA/H+b4XQCN4xOWGzAqG0iNg672ywrfqsIkwVYNn1arcdMREREvu2P7nhR+jjA3fOY4LCmCFTKkP9FaqUFdslt6atSgb53z7cK9i4H7BaEDZ5brkHWzc+XwCYaYPxpH4T7/uJ6juXUN3h3+TsY078d7Gcuwapues/vZ1jfXm5FtPHYCfj2JjdEdYOFcANV363mS/4Y5duAB1uGorXmNHDmvNtjruTr0TU6CABc3aKtdic0i/VoHqJDkc2GwZtMrnXEwQE6LJv+D64PJiIiojp1r0V1z94DodVqsXb1Ctf51gsvvISU5Fc8T5k2GxD90nYUHPkMuTuS4CgyoWmLlvjni3MxbPggwHi+lt4REVWFhXADVltXSmvLg83Dcfiisdz054f+FIE9qekSRkZERER0b/o+PRSdu/R2u+2TjNUep0yXNMgK7T4Gwn1/AQ6vxdoNO2ERBADOeo6cyL+xDRHVm06PdcSwLBGKRYV4dKUBz24y4tlME05fzcVfx8XhzXVrpA6RiIiI6A/z1MyrdIMsoOZNTLftz0GPUQlo1n0geoxKwLb9OVU+x3jsxL2ET+QXOCJM9WLX4a/xzf8ewrYRxWuA3zxUhHXHrNgeF+BaEzxq2z4AwOsTnpc4WiIiIqJ7V3aJmkIb6NYgC6i4ianhxzOwKXPdbtv5wzEs37UVHw5Somt0EA5fNGB82goUnb+Mwf/TodJYuD0ckWcshKlerNryGZ5r68D0vRb8nOdAIw2wdUSAa5p0r5ZKbBwmIGZzNgthIiIi8nmll6gdPLAH6empMJdqkFVZE9OyxevK7GX4cJDS7bzpw0HAlOxsDHwmpm7fCFEDJWkhrNfrkZiYCIPBAKvVildeeQUdOlR+VYu8z8EDe/BJxmrcuHIe4S3ux9TZs9F6eFu3x5y+mos1BYBOIwMAFJiBrtHu2yJ1jVagQGRfQ5IecxMREdWmajcxPXcWNmVIueefuZbnajhaomu0Ameu5dVZzEQNnaSF8Pr169G5c2ckJCTgt99+w5w5c7B9+3YpQ6IaKrnCqXtqOqLuXOFclpSEPynHY0TbP7seF6CSQasC1gwqnhr9yAcGj/sGhwjcM5ikx9xERES17Y80Ma2o4eiDzcNrKzwivyNps6yEhASMHDkSAGC326HRaKQMh+7BJxmroXtqOrT3tYNMoYT2vnbQ9Z2OpOWfuj1OJnPioyECerVUQqWQ4a1eWiR8LiLnnA1WuxM552wYtU3EM937SPROiO5ibiIiIm8yeXg8xu92uJ03jd/twOTh8VKHRlRvHA4HFi5ciLi4OIwZMwYXLlxwu3/37t2IjY3FyJEjsXDhQjgcjkqPV28jwps3b0ZGRobbbcnJyWjXrh1yc3ORmJiI+fPnV3kcuVwOnU5X49dXKBT39Lz65mtx3rhyHlEe9sm7lHURgiCgKEDAjqP/C6PFfSp0/GMq2BxO177BIYICw/oMxJJp02s3TrkCwcFVf55OtQaCEAA11BAEJXTa+pss4Ws/84ZG6twE+M5n6wtxVhajXiGHIMihUWsgBAiQVyM31JXq5iZHoQCNWgOHIECtcUCnrN+JXL7wMwd8J07yTqWXeDVpcT9Gj5vkVdtPAsCgrt0BAFO2fIYz1/LwYPNwzBgd77qdyB9kZ2fDYrEgMzMTx48fx9KlS5GeXrwFq9lsRmpqKnbt2gVBEDB79mzk5OSgd+/eFR6v3v6ixsbGIjY2ttztp06dwuzZszF37lx06tSpyuM4HA7o9foav75Op7un59U3X4uzSYv7Pe6TF3VfNERRhGgSsfzjDxGqBR75wIBzBU48Ei7Hgm4aRAbL0TRQBkuAErPHTsGgrt1RWFi77z04WFetY2osRRBFEywKC0TRCr21VsOolK/9zBsaqXMT4DufrS/EWVmMFkGAKDohtxRBNImw13K+qYnq5iaFSUSARQVRFGEpkkEvivUQ3V2+8DMHfCdO8j6elnilp6cCgFcWwyx8yVtY7A5czDfX+nEfr+S+o0ePolu3bgCA9u3b48SJu9uDqdVqbNq0CYIgAABsNluVM/okXSN89uxZzJgxA6mpqXj44YelDIXu0ehxk4r/YDw1/W4XxP1pWPT6eNdjTl/NRWSwzLU++PBFOxI+F2G2OpH6tIBmQTJM2fIZkzt5DeYmIiL/UHqJF4Di/z41HZ9krPa6QpjImygABNdBa5/8/HxMnDjR9X1cXBzi4or33zYYDAgKuts0TqFQwGazQalUQi6XIzy8eM38xx9/DJPJhC5dulT6WpIWwikpKbBYLEhKSgIABAUFuYa3yTeU7YIY3uJ+JC5YgOFD2wJnzgMo7hSdMVRwa/n/0RAB43eKiH9MBavdya6H5FWYm4iI/EOFS7yunJcmICI/17hxY2zbts3jfUFBQTAaja7vHQ4HlKWWDDkcDixbtgznzp1DWloaZDJZpa8laSHME8uGoXQXxHwb8GDLUACnXffri5wet0q6eNsJgF0PyfswNxER+YeKlng1aXG/dEERkUcdO3ZETk4OBgwYgOPHj+Ohhx5yu3/hwoVQq9VYuXIl5PKqe0JL2jWa/EOIUDwdurTDF+14JFyOnHM2jNtpZddDIiIiqnejx02Cfl8azBd+gtNug/nCT9DvS8PocZOkDo2Iyujbty/UajVGjhyJJUuWYN68edi1axcyMzPx3//+F1u2bMHp06cxbtw4jBkzBvv376/0eJKOCJN/eKZ7H8Rv3YfPYu6uEX5um4jfDU4MzTThzUkvcn0wERER1buyS7yatLgfU6bM5PpgIi8kl8uxaNEit9seeOAB19e//PJLjY7HQpjqXMfWrfHJV19hWJYJhUXAI+FyjO+gwsb/WHG+wMkimIiIiCRTeomXFPJtwJ9DTFAb86A//isgi5AsFiJ/wkKY6tTOH45hxe7taBUqw8hHVdjxiw0/5zmAX2wY9ZgKW35tJHWIRERERHUu3wYE6YRyt/85xIQo1XWYt38Fh10Fe2QrCaIj8j8shKlOrfzqS3z4jBy/G7RY8G8z1g2+Oz16/G4HZozm2mAiIiJq2EqK4DbRNoRo3RuIqo15riK4KLKjRBES+R8WwlSnzty4ha7ROqgUxe3Lp+814+c8BwJUwFuTuTaYiIiIGrbSRXCzs4eggKPcY0wsgonqHQthqlMPNgnF4YtF6NVSifjHVIh/TIWcczZMyQ5kEUxEREQNXuki2HD0PGzKkHKPsbMIJqp33D6J6tTUfk9j/G4Hcs7ZYLU7kXPOhvG7HdwuiYiIiBq8fBvQPFyLEK0CCjhgU4bA3qxVuX9EVP84Ikx1avD/dIAjJAJTtnyGM9fy8GDzcMwYHc/RYCIiIiIikgwLYapzg7p2Z+FLREREREReg1OjiYiIiIiIyK+wECYiIiIiIiK/wkKYiIiIiIiI/AoLYSIiIiIiIvIrbJZFRERERERE1VZksePs+cJaP27vx2v9kBViIUxERERERETVppLL8CedSuow/hBOjSYiIiIiIiK/wkKYiIiIiIiI/AqnRhMRERH5CL1ej8TERBgMBlitVrzyyivo0KGD1GEREfkcFsJEREREPmL9+vXo3LkzEhIS8Ntvv2HOnDnYvn271GEREfkcFsJEREREPiIhIQFqtRoAYLfbodFoJI6IiMg3sRAmIiIi8kKbN29GRkaG223Jyclo164dcnNzkZiYiPnz51d5HLlcDp1OV1dh1hqFQuETcZZVWdyi2QZBECAIgEatgRAgQB7sHe9RIVcguJqxOAoFCIIAm1oNQS5AEMzQaeu/jGiIvyMkHRbCRERERF4oNjYWsbGx5W4/deoUZs+ejblz56JTp05VHsfhcECv19dFiLVKp9P5RJxlVRa3aANEUQtRFCG3FEE0ibAXesd7DA7WobCasShMIjSiCIfFArFIhCiK0FvrOEAPGuLvCEmHhTARERGRjzh79ixmzJiB1NRUPPzww1KHQ0Tks1gIExEREfmIlJQUWCwWJCUlAQCCgoKQnp4ucVRERL6HhTARERGRj2DRS0RUO+RSB0BERERERERUn1gIExERERERkV9hIUxERERERER+RdI1wiaTCXPmzMHt27chCAKWLVuGxo0bSxkSERFzExEREVEDJ+mIcFZWFtq2bYuNGzdi4MCBWLlypZThEBEBYG4iIiIi8jYOhwMLFy5EXFwcxowZgwsXLrjd/+9//xsxMTGIi4tDVlZWlceTdEQ4ISEBdrsdAHD16lWEh4dLGQ4REQDmJiIiIiJvk52dDYvFgszMTBw/fhxLly51ddK3Wq1YsmQJtmzZAkEQEB8fj169eiEiIqLC49VbIbx582ZkZGS43ZacnIx27dph7NixOH36NNavX19f4RARAWBuIiIiIvIFR48eRbdu3QAA7du3x4kTJ1z3/frrr4iOjkajRo0AAI8//jh++OEH9O/fv8Lj1VshHBsbi9jYWI/3bdiwAb/++ismTZqE7OzsSo8jl8uh0+lq/PoKheKenlfffD1O0WyDIAjQKNVQCgKKAgTIg6V7Pwq5AsHVeH2nWgNBCIAaagiCEjpt/U2W8PWfua+TOjcBvvPZ+kKclcWoV8ghCHJo1BoIPpKbHIUCNGoNHIIAtcYBnbJ+J3L5ws8c8J04iYgaipBGARj01GO1ftxr165h2rRpru/j4uIQFxcHADAYDAgKCnLdp1AoYLPZoFQqYTAY3P4OBAYGwmAwVPpakk6NXr16NZo2bYqhQ4ciICAACoWiyuc4HA7o9foav5ZOp7un59U3X49TtAGiqEWRxgKrKEI0ibAXSvd+goN1KKzG62ssRRBFEywKC0TRCr21HoK7w9d/5g1RfeYmwHc+W1+Is7IYLYIAUXRCbinymdykMIkIsKggiiIsRTLoRbEeorvLF37mgO/ESURElWvevDm2bdvm8b6goCAYjUbX9w6HA8o7F4jL3mc0Gqu8QCppIRwTE4OXX34ZW7duhd1uR3JyspThEBEBYG4iIiIi8jYdO3ZETk4OBgwYgOPHj+Ohhx5y3ffAAw/gwoULKCgoQEBAAH744QdMmDCh0uNJWgiHh4dj3bp1UoZARFQOcxMRERGRd+nbty+OHDmCkSNHwul0Ijk5Gbt27YLJZEJcXBxeeeUVTJgwAU6nEzExMWjatGmlx5O0ECYiIiIiIiKqilwux6JFi9xue+CBB1xfP/nkk3jyySerf7xai4yIiIiIiIjIB7AQJiIiIiIiIr/CQpiIiIiIiIj8CgthIiIiIiIi8isyp9PplDoIIiIiIiIiovrCEWEiIiIiIiLyKyyEiYiIiIiIyK+wECYiIiIiIiK/wkKYiIiIiIiI/AoLYSIiIiIiIvIrLISJiIiIiIjIr/hNIWwymTBlyhSMGjUKEyZMQH5+vtQheaTX6zF58mSMHj0acXFxOHbsmNQhVWr//v2YM2eO1GG4cTgcWLhwIeLi4jBmzBhcuHBB6pAq9eOPP2LMmDFSh1Ehq9WKxMREjBo1CsOHD8eBAwekDqnB8YX8xNxUO3wpPzE3UX3ztTxTlrfmndJ8KQeV5e05qSzmKO/nN4VwVlYW2rZti40bN2LgwIFYuXKl1CF5tH79enTu3BmffPIJlixZgkWLFkkdUoUWL16MlJQUOBwOqUNxk52dDYvFgszMTMyZMwdLly6VOqQKrVmzBq+++iqKioqkDqVCO3fuREhICDZu3Ig1a9bgrbfekjqkBscX8hNzU+3wlfzE3ERS8KU8U5Y3553SfCUHleULOaks5ijvp5Q6gPqSkJAAu90OALh69SrCw8MljsizhIQEqNVqAIDdbodGo5E4oop17NgRffr0QWZmptShuDl69Ci6desGAGjfvj1OnDghcUQVi46ORlpaGubOnSt1KBV6+umn0a9fP9f3CoVCwmgaJl/IT8xNtcNX8hNzE0nBl/JMWd6cd0rzlRxUli/kpLKYo7xfgyyEN2/ejIyMDLfbkpOT0a5dO4wdOxanT5/G+vXrJYrursrizM3NRWJiIubPny9RdHdVFOeAAQPw/fffSxRVxQwGA4KCglzfKxQK2Gw2KJXe9+ver18/XL58WeowKhUYGAig+HN98cUXMXPmTIkj8m2+kJ+Ym+qOr+Qn5iaqa76SZ8ryxbxTmq/koLJ8ISeVxRzl/bz7t/4excbGIjY21uN9GzZswK+//opJkyYhOzu7niNzV1Gcp06dwuzZszF37lx06tRJgsjcVfZ5eqOgoCAYjUbX9w6Hw+sTvLe7du0apk2bhlGjRmHQoEFSh+PTfCE/MTfVHean2sXc5Lt8Jc+U5Yt5pzTmoPrFHOXd/GaN8OrVq7Fjxw4AQEBAgNdOTzh79ixmzJiBlJQU9OjRQ+pwfFLHjh3x9ddfAwCOHz+Ohx56SOKIfFteXh7Gjx+PxMREDB8+XOpwGiRfyE/MTbWD+an2MDc1PMwzdY85qP4wR3k/v7kEFBMTg5dffhlbt26F3W5HcnKy1CF5lJKSAovFgqSkJADFV+7S09Mljsq39O3bF0eOHMHIkSPhdDq99mftK1atWoXCwkKsXLnS1cRpzZo10Gq1EkfWcPhCfmJuqh3MT7WHuanhYZ6pe8xB9Yc5yvvJnE6nU+ogiIiIiIiIiOqL30yNJiIiIiIiIgJYCBMREREREZGfYSFMREREREREfoWFMBEREREREfkVFsJERERERETkV1gIkyR++eUXPProo/j000/dbrfZbBg2bBheeuklt9tXrlxZ7jYiorrA/ERE3oi5iah2sRAmSTz88MOYOHEiUlJScPXqVdft77//PnJzc/Haa6+5btu9ezfef/99KcIkIj/E/ERE3oi5iah2sRAmyUydOhXNmzfHwoULAQA//vgj1qxZg6SkJDRq1Ag2mw2vv/465s+fj6ioKImjJSJ/wvxERN6IuYmo9rAQJsmo1WokJSXhyJEj2LFjB+bPn4+YmBh0794dAGAymfDbb78hKysLHTp0kDhaIvInzE9E5I2Ym4hqj1LqAMi/tW/fHqNHj8aCBQvQrFkzvPzyy677goOD8fHHH0sYHRH5M+YnIvJGzE1EtYMjwiS5Xr16wWazoU2bNggMDJQ6HCIiF+YnIvJGzE1EfxwLYZKUKIp444030KlTJ+zbtw/Z2dlSh0REBID5iYi8E3MTUe1gIUySevfdd2E2m7Fy5UoMGTIEb7zxBgoLC6UOi4iI+YmIvBJzE1HtYCFMkvnuu+/w6aef4s0334ROp8O8efNgt9uxdOlSqUMjIj/H/ERE3oi5iaj2sBAmSRgMBsyfPx/PPPMMevXqBQAIDQ3FvHnzsHXrVhw5ckTiCInIXzE/EZE3Ym4iql0shEkSb7/9NsxmMxYsWOB2++DBg9GjRw+89tprMBqNEkVHRP6M+YmIvBFzE1HtkjmdTqfUQRARERERERHVF44IExERERERkV9hIUxERERERER+hYUwERERERER+RUWwkRERERERORXWAgTERERERGRX2EhTERERERERH6FhTARERERERH5FRbCRERERERE5FdYCBMREREREZFf+f/Hx9ZYRu2ldAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SMOTE provides higher performances than random oversampling for the three metrics. The Average Precision however remains lower than the baseline classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ROS</th>\n",
       "      <td>0.019+/-0.01</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.88+/-0.038</td>\n",
       "      <td>0.456+/-0.062</td>\n",
       "      <td>0.888+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - SMOTE</th>\n",
       "      <td>0.022+/-0.012</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.913+/-0.032</td>\n",
       "      <td>0.499+/-0.056</td>\n",
       "      <td>0.91+/-0.019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.008+/-0.002  0.008+/-0.005  0.906+/-0.025   \n",
       "Decision tree - ROS        0.019+/-0.01  0.008+/-0.003   0.88+/-0.038   \n",
       "Decision tree - SMOTE     0.022+/-0.012  0.008+/-0.003  0.913+/-0.032   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  \n",
       "Decision tree - ROS          0.456+/-0.062      0.888+/-0.03  \n",
       "Decision tree - SMOTE        0.499+/-0.056      0.91+/-0.019  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_ROS,\n",
    "           results_df_SMOTE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other oversampling strategies\n",
    "\n",
    "There exists a range of more sophisticated strategies for oversampling, whose details go beyond the scope of this book. We refer the reader to {cite}`fernandez2018learning` for a review and to the [`imblearn` page on oversampling methods](https://imbalanced-learn.org/stable/references/over_sampling.html) for their implementations in Python. In particular the `imblearn` library provides the following additional oversampling methods: `SMOTENC` {cite}`chawla2002smote`, `SMOTEN` {cite}`chawla2002smote`, `ADASYN` {cite}`he2008adasyn`, `BorderlineSMOTE` {cite}`han2005borderline`, `KMeansSMOTE` {cite}`last2017oversampling`, and `SVMSMOTE` {cite}`nguyen2011borderline`. These methods can be used by simply replacing the sampler with the desired method in the code above. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies_Undersampling)=\n",
    "### Undersampling\n",
    "\n",
    "Undersampling refers to the process of reducing the number of samples in the majority class. The naive approach, called *random undersampling* (RUS), consists in randomly removing samples from the majority class until the desired imbalance ratio is achieved. \n",
    "\n",
    "The major drawback of RUS is that the method may discard samples that are important for identifying the decision boundary. A range of more advanced techniques have been proposed that aim at removing samples in a more principled way {cite}`fernandez2018learning`. Besides RUS, the `imblearn` package proposes no less than ten different methods for undersampling {cite}`Imblearn`. Most of these methods rely on nearest neighbors heuristics that remove samples when they either lie close or far away from other samples. We refer the reader to {cite}`fernandez2018learning,Imblearn` for the detailed algorithms underlying more advanced undersampling methods.\n",
    "\n",
    "The next two subsections show how two of these methods can be implemented, and illustrate their ability to move the decision boundary towards the minority class. As examples, we rely on RUS, and Edited Nearest Neighbor (ENN).\n",
    "\n",
    "\n",
    "(Resampling_Strategies_RUS)=\n",
    "#### Random undersampling\n",
    "\n",
    "The `imblearn` sampler for RUS is [`imblearn.under_sampling.RandomUnderSampler`](https://imbalanced-learn.org/dev/references/generated/imblearn.under_sampling.RandomUnderSampler.html). Let us illustrate its use and its impact on the classifier decision boundary and the classification performances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "sampler_list = [('sampler', imblearn.under_sampling.RandomUnderSampler(sampling_strategy=1, random_state=0))]\n",
    "\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_RUS, classifier_0, train_df, test_df) = kfold_cv_with_sampler_and_classifier(classifier, \n",
    "                                                                                         sampler_list, \n",
    "                                                                                         X, y, \n",
    "                                                                                         n_splits=5,\n",
    "                                                                                         strategy_name=\"Decision tree - RUS\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a `sampling_strategy` set to one, RUS randomly removes samples from the majority class until their number reaches that of the minority class. After resampling, each class contains 216 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    216\n",
       "1    216\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotting of training samples shows that the number of samples from the majority class was significantly reduced, allowing a shift of the decision boundary towards the minority class. Contrary to oversampling techniques, the region located at the bottom right is now associated with the minority class, since all samples of class 0 from this region have been removed.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performances in terms of AUC ROC and balanced accuracy are on par with oversampling techniques. We however note a loss of performance in terms of Average Precision. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ROS</th>\n",
       "      <td>0.019+/-0.01</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.88+/-0.038</td>\n",
       "      <td>0.456+/-0.062</td>\n",
       "      <td>0.888+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - SMOTE</th>\n",
       "      <td>0.022+/-0.012</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.913+/-0.032</td>\n",
       "      <td>0.499+/-0.056</td>\n",
       "      <td>0.91+/-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - RUS</th>\n",
       "      <td>0.005+/-0.001</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.913+/-0.02</td>\n",
       "      <td>0.408+/-0.058</td>\n",
       "      <td>0.896+/-0.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.008+/-0.002  0.008+/-0.005  0.906+/-0.025   \n",
       "Decision tree - ROS        0.019+/-0.01  0.008+/-0.003   0.88+/-0.038   \n",
       "Decision tree - SMOTE     0.022+/-0.012  0.008+/-0.003  0.913+/-0.032   \n",
       "Decision tree - RUS       0.005+/-0.001  0.006+/-0.002   0.913+/-0.02   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  \n",
       "Decision tree - ROS          0.456+/-0.062      0.888+/-0.03  \n",
       "Decision tree - SMOTE        0.499+/-0.056      0.91+/-0.019  \n",
       "Decision tree - RUS          0.408+/-0.058     0.896+/-0.023  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_ROS,\n",
    "           results_df_SMOTE,\n",
    "           results_df_RUS])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is worth noting that the training time with RUS is faster. This results from the simple undersampling procedure and the smaller training set size. The method is therefore useful not only for reducing the imbalance ratio but also for speeding up the execution time of the modeling procedure.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Edited Nearest Neighbor \n",
    "\n",
    "The Edited Nearest Neighbor rule is an undersampling technique that removes samples from the majority class in overlapping regions of the dataset {cite}`laurikkala2001improving,wilson1972asymptotic`. It is based on a nearest neighbor rule, that removes majority class samples as follows {cite}`fernandez2018learning` (Chapter 5, Page 84):\n",
    "\n",
    "* For each majority class sample, the k-nearest neighbors are found. If the majority of these samples are from the minority class, the majority class sample is removed.\n",
    "* For each minority class sample, the k-nearest neighbors are found. If the majority of these samples are from the majority class, the majority class sample(s) is (are) removed.\n",
    "\n",
    "The number of neighbors $k$ is by default set to $k=3$. It is worth noting that, contrary to RUS, the number of majority class samples that are removed depends on the degree of overlap between the two classes. The method does not allow to specify an imbalanced ratio. \n",
    "\n",
    "The `imblearn` sampler for RUS is [`imblearn.under_sampling.EditedNearestNeighbours`](https://imbalanced-learn.org/stable/references/generated/imblearn.under_sampling.EditedNearestNeighbours.html). Let us illustrate its use and its impact on the classifier decision boundary and the classification performances. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "sampler_list = [('sampler', imblearn.under_sampling.EditedNearestNeighbours(sampling_strategy='majority',n_neighbors=3))]\n",
    "\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_ENN, classifier_0, train_df, test_df) = kfold_cv_with_sampler_and_classifier(classifier, \n",
    "                                                                                         sampler_list, \n",
    "                                                                                         X, y, \n",
    "                                                                                         n_splits=5,\n",
    "                                                                                         strategy_name=\"Decision tree - ENN\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a number of neighbors `n_neighbors` set to three, ENN only removes around 200 samples out of the 3784 samples of the majority class. The number of samples from the minority class is unchanged. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3572\n",
       "1     216\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotting of training samples shows that the distributions of the two classes are similar to the original distributions. The samples that were removed lied in the overlapping region, and their removal is therefore not visible due to the large amount of remaining samples. We however observe a shift in the decicision boundary, since the decision tree now classifies samples from the overlapping region into the minority class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On this dataset, the performances of ENN are poor compared to the previsouly tested techniques. The balanced accuracy was slightly improved compared to the baseline classifier. The performance in terms of AP is however lower than the baseline, and the AUC ROC is the worst of all tested tecniques (and on par with ROS). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ROS</th>\n",
       "      <td>0.019+/-0.01</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.88+/-0.038</td>\n",
       "      <td>0.456+/-0.062</td>\n",
       "      <td>0.888+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - SMOTE</th>\n",
       "      <td>0.022+/-0.012</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.913+/-0.032</td>\n",
       "      <td>0.499+/-0.056</td>\n",
       "      <td>0.91+/-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - RUS</th>\n",
       "      <td>0.005+/-0.001</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.913+/-0.02</td>\n",
       "      <td>0.408+/-0.058</td>\n",
       "      <td>0.896+/-0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ENN</th>\n",
       "      <td>0.023+/-0.004</td>\n",
       "      <td>0.009+/-0.005</td>\n",
       "      <td>0.879+/-0.038</td>\n",
       "      <td>0.474+/-0.08</td>\n",
       "      <td>0.857+/-0.041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.008+/-0.002  0.008+/-0.005  0.906+/-0.025   \n",
       "Decision tree - ROS        0.019+/-0.01  0.008+/-0.003   0.88+/-0.038   \n",
       "Decision tree - SMOTE     0.022+/-0.012  0.008+/-0.003  0.913+/-0.032   \n",
       "Decision tree - RUS       0.005+/-0.001  0.006+/-0.002   0.913+/-0.02   \n",
       "Decision tree - ENN       0.023+/-0.004  0.009+/-0.005  0.879+/-0.038   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  \n",
       "Decision tree - ROS          0.456+/-0.062      0.888+/-0.03  \n",
       "Decision tree - SMOTE        0.499+/-0.056      0.91+/-0.019  \n",
       "Decision tree - RUS          0.408+/-0.058     0.896+/-0.023  \n",
       "Decision tree - ENN           0.474+/-0.08     0.857+/-0.041  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_ROS,\n",
    "           results_df_SMOTE,\n",
    "           results_df_RUS,\n",
    "           results_df_ENN])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other undersampling strategies\n",
    "\n",
    "As for ovesampling techniques, we refer the reader to {cite}`fernandez2018learning` for a review of more sophisticated undersampling techniques and to the [`imblearn` page on undersampling methods](https://imbalanced-learn.org/stable/references/under_sampling.html) for their implementations in Python. In particular the `imblearn` library provides ten other undersampling methods, which can be tested by simply replacing the sampler with the desired method in the code above. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining over and undersampling\n",
    "\n",
    "Oversampling and undersampling are often complementary. On the one hand, oversampling techniques allow to generate synthetic samples from the minority class, and help a classifier in identifying more precisely the decision boundary between the two classes. On the other hand, undersampling techniques reduce the size of the training set, and allow to speed-up the classifier training time.  Combining over and undersampling techniques has often been reported to successfully improve the classifier performances (Chapter 5, Section 6 in {cite}fernandez2018learning).\n",
    "\n",
    "In terms of implementation, the combination of samplers is obtained by chaining the samplers in a `pipeline`. The samplers can then be chained to a classifer. We illustrate below the chaining of an SMOTE oversampling to a random undersampling to a decision tree classifier. \n",
    "\n",
    "```\n",
    "sampler_list = [('sampler1', imblearn.over_sampling.SMOTE(sampling_strategy=0.5,random_state=0)),\n",
    "                ('sampler2', imblearn.under_sampling.RandomUnderSampler(sampling_strategy=1.0,random_state=0))\n",
    "               ]\n",
    "\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "estimators = sampler_list.extend([('clf', classifier)])\n",
    "    \n",
    "pipe = imblearn.pipeline.Pipeline(estimators)\n",
    "```\n",
    "\n",
    "The `kfold_cv_with_sampler_and_classifier` takes the `sampler_list` and the `classifier` as two separate arguments, and takes care of chaining the samplers and classifier together. We can therefore follow the same template as before for assessing the performances of a classifier based on a chain of resampling techniques. The samplers are provided as a list with the `sampler_list` variable.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "sampler_list = [('sampler1', imblearn.over_sampling.SMOTE(sampling_strategy=0.5,random_state=0)),\n",
    "                ('sampler2', imblearn.under_sampling.RandomUnderSampler(sampling_strategy=1.0,random_state=0))\n",
    "               ]\n",
    "\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5, random_state=0)\n",
    "\n",
    "(results_df_combined, classifier_0, train_df, test_df) = kfold_cv_with_sampler_and_classifier(classifier, \n",
    "                                                                                              sampler_list, \n",
    "                                                                                              X, y, \n",
    "                                                                                              n_splits=5,\n",
    "                                                                                              strategy_name='Decision tree - Combined SMOTE and RUS')\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SMOTE oversampling aimed at an imbalance ratio of 0.5. Since the original dataset contains 3784 sample of the majority class, SMOTE create new samples until the number of minority class samples reached $3784/2=1892$ samples. The random undersampling aimed at an imbalance ratio of 1. Since the SMOTE resampled dataset contains 1892 samples of the minority class, samples of the majority class are removed until their number reached 1892 samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1892\n",
       "1    1892\n",
       "Name: Y, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting decision boundary is close to that obtained with SMOTE, except that a slightly larger region is now considered to be of class 1 by the classifier. This is coherent since less samples of the minority class were created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting performances are on par with those of SMOTE. For larger dataset, one should however expect faster training times than SMOTE, since the training set size is decreased thanks to undersampling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.008+/-0.005</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ROS</th>\n",
       "      <td>0.019+/-0.01</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.88+/-0.038</td>\n",
       "      <td>0.456+/-0.062</td>\n",
       "      <td>0.888+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - SMOTE</th>\n",
       "      <td>0.022+/-0.012</td>\n",
       "      <td>0.008+/-0.003</td>\n",
       "      <td>0.913+/-0.032</td>\n",
       "      <td>0.499+/-0.056</td>\n",
       "      <td>0.91+/-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - RUS</th>\n",
       "      <td>0.005+/-0.001</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.913+/-0.02</td>\n",
       "      <td>0.408+/-0.058</td>\n",
       "      <td>0.896+/-0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - ENN</th>\n",
       "      <td>0.023+/-0.004</td>\n",
       "      <td>0.009+/-0.005</td>\n",
       "      <td>0.879+/-0.038</td>\n",
       "      <td>0.474+/-0.08</td>\n",
       "      <td>0.857+/-0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - Combined SMOTE and RUS</th>\n",
       "      <td>0.019+/-0.005</td>\n",
       "      <td>0.008+/-0.001</td>\n",
       "      <td>0.915+/-0.012</td>\n",
       "      <td>0.494+/-0.062</td>\n",
       "      <td>0.912+/-0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Fit time (s) Score time (s)  \\\n",
       "Decision tree - Baseline                0.008+/-0.002  0.008+/-0.005   \n",
       "Decision tree - ROS                      0.019+/-0.01  0.008+/-0.003   \n",
       "Decision tree - SMOTE                   0.022+/-0.012  0.008+/-0.003   \n",
       "Decision tree - RUS                     0.005+/-0.001  0.006+/-0.002   \n",
       "Decision tree - ENN                     0.023+/-0.004  0.009+/-0.005   \n",
       "Decision tree - Combined SMOTE and RUS  0.019+/-0.005  0.008+/-0.001   \n",
       "\n",
       "                                              AUC ROC Average Precision  \\\n",
       "Decision tree - Baseline                0.906+/-0.025     0.528+/-0.072   \n",
       "Decision tree - ROS                      0.88+/-0.038     0.456+/-0.062   \n",
       "Decision tree - SMOTE                   0.913+/-0.032     0.499+/-0.056   \n",
       "Decision tree - RUS                      0.913+/-0.02     0.408+/-0.058   \n",
       "Decision tree - ENN                     0.879+/-0.038      0.474+/-0.08   \n",
       "Decision tree - Combined SMOTE and RUS  0.915+/-0.012     0.494+/-0.062   \n",
       "\n",
       "                                       Balanced accuracy  \n",
       "Decision tree - Baseline                   0.786+/-0.046  \n",
       "Decision tree - ROS                         0.888+/-0.03  \n",
       "Decision tree - SMOTE                       0.91+/-0.019  \n",
       "Decision tree - RUS                        0.896+/-0.023  \n",
       "Decision tree - ENN                        0.857+/-0.041  \n",
       "Decision tree - Combined SMOTE and RUS     0.912+/-0.005  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_ROS,\n",
    "           results_df_SMOTE,\n",
    "           results_df_RUS,\n",
    "           results_df_ENN,\n",
    "           results_df_combined])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies_Transaction_Data)=\n",
    "## Transaction data\n",
    "\n",
    "Let us now apply resampling techniques to the simulated dataset of transaction data. We reuse the methodology of [Chapter 5, Model Selection](Model_Selection), using prequential validation as the validation strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data\n",
    "\n",
    "The loading of data and initialization of the parameters follow the same template as in [Chapter 5, Model Selection](Model_Selection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load  files\n",
      "CPU times: user 812 ms, sys: 580 ms, total: 1.39 s\n",
      "Wall time: 1.63 s\n",
      "919767 transactions loaded, containing 8195 fraudulent transactions\n"
     ]
    }
   ],
   "source": [
    "# Load data from the 2018-07-11 to the 2018-09-14\n",
    "\n",
    "DIR_INPUT = 'simulated-data-transformed/data/' \n",
    "\n",
    "BEGIN_DATE = \"2018-06-11\"\n",
    "END_DATE = \"2018-09-14\"\n",
    "\n",
    "print(\"Load  files\")\n",
    "%time transactions_df = read_from_files(DIR_INPUT, BEGIN_DATE, END_DATE)\n",
    "print(\"{0} transactions loaded, containing {1} fraudulent transactions\".format(len(transactions_df),transactions_df.TX_FRAUD.sum()))\n",
    "\n",
    "\n",
    "# Number of folds for the prequential validation\n",
    "n_folds = 4\n",
    "\n",
    "# Set the starting day for the training period, and the deltas\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train = delta_delay = delta_test = delta_valid = delta_assessment = 7\n",
    "\n",
    "start_date_training_for_valid = start_date_training+datetime.timedelta(days=-(delta_delay+delta_valid))\n",
    "start_date_training_for_test = start_date_training+datetime.timedelta(days=(n_folds-1)*delta_test)\n",
    "\n",
    "output_feature = \"TX_FRAUD\"\n",
    "\n",
    "input_features = ['TX_AMOUNT','TX_DURING_WEEKEND', 'TX_DURING_NIGHT', 'CUSTOMER_ID_NB_TX_1DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_1DAY_WINDOW', 'CUSTOMER_ID_NB_TX_7DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_7DAY_WINDOW', 'CUSTOMER_ID_NB_TX_30DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_30DAY_WINDOW', 'TERMINAL_ID_NB_TX_1DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_1DAY_WINDOW', 'TERMINAL_ID_NB_TX_7DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_7DAY_WINDOW', 'TERMINAL_ID_NB_TX_30DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_30DAY_WINDOW']\n",
    "\n",
    "\n",
    "# Only keep columns that are needed as argument to the custom scoring function\n",
    "# (in order to reduce the serialization time of transaction dataset)\n",
    "transactions_df_scorer = transactions_df[['CUSTOMER_ID', 'TX_FRAUD','TX_TIME_DAYS']]\n",
    "\n",
    "card_precision_top_100 = sklearn.metrics.make_scorer(card_precision_top_k_custom, \n",
    "                                                     needs_proba=True, \n",
    "                                                     top_k=100, \n",
    "                                                     transactions_df=transactions_df_scorer)\n",
    "\n",
    "performance_metrics_list_grid = ['roc_auc', 'average_precision', 'card_precision@100']\n",
    "performance_metrics_list = ['AUC ROC', 'Average precision', 'Card Precision@100']\n",
    "\n",
    "scoring = {'roc_auc':'roc_auc',\n",
    "           'average_precision': 'average_precision',\n",
    "           'card_precision@100': card_precision_top_100,\n",
    "           }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a baseline, let us compute the fraud detection performances with a decision tree of depth 5 without any resampling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.tree.DecisionTreeClassifier()\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__max_depth':[5], 'clf__random_state':[0]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df=model_selection_wrapper(transactions_df, classifier, \n",
    "                                        input_features, output_feature,\n",
    "                                        parameters, scoring, \n",
    "                                        start_date_training_for_valid,\n",
    "                                        start_date_training_for_test,\n",
    "                                        n_folds=n_folds,\n",
    "                                        delta_train=delta_train, \n",
    "                                        delta_delay=delta_delay, \n",
    "                                        delta_assessment=delta_assessment,\n",
    "                                        performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                        performance_metrics_list=performance_metrics_list,\n",
    "                                        n_jobs=1)\n",
    "\n",
    "execution_time_dt = time.time()-start_time\n",
    "\n",
    "# Select parameter of interest (max_depth)\n",
    "parameters_dict=dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary']=[parameters_dict[i]['clf__max_depth'] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_dt for model performance comparison at the end of this notebook\n",
    "performances_df_dt=performances_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.804+/-0.02</td>\n",
       "      <td>0.546+/-0.04</td>\n",
       "      <td>0.268+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.81+/-0.01</td>\n",
       "      <td>0.6+/-0.02</td>\n",
       "      <td>0.284+/-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.81+/-0.01</td>\n",
       "      <td>0.6+/-0.02</td>\n",
       "      <td>0.284+/-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters             5                 5                  5\n",
       "Validation performance     0.804+/-0.02      0.546+/-0.04       0.268+/-0.01\n",
       "Test performance            0.81+/-0.01        0.6+/-0.02        0.284+/-0.0\n",
       "Optimal parameter(s)                  5                 5                  5\n",
       "Optimal test performance    0.81+/-0.01        0.6+/-0.02        0.284+/-0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_dt=get_summary_performances(performances_df_dt, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This baseline will be used at the end of the notebook to assess the benefits of different resampling techniques.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prequential validation with resampling\n",
    "\n",
    "The addition of resampling to the prequential validation simply consists in extending the pipeline defined in [Chapter 5, Validation Strategies](Sklearn_Validation_Pipeline) with the sampler objects. We add this step at the beginning of the `prequential_grid_search` function and rename it as `prequential_grid_search_with_sampler`. Note that the pipeline is created with the `Pipeline` object from the `imblearn` module so that samplers are properly processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def prequential_grid_search_with_sampler(transactions_df, \n",
    "                                         classifier, sampler_list,\n",
    "                                         input_features, output_feature, \n",
    "                                         parameters, scoring, \n",
    "                                         start_date_training, \n",
    "                                         n_folds=4,\n",
    "                                         expe_type='Test',\n",
    "                                         delta_train=7, \n",
    "                                         delta_delay=7, \n",
    "                                         delta_assessment=7,\n",
    "                                         performance_metrics_list_grid=['roc_auc'],\n",
    "                                         performance_metrics_list=['AUC ROC'],\n",
    "                                         n_jobs=-1):\n",
    "    \n",
    "    estimators = sampler_list.copy()\n",
    "    estimators.extend([('clf', classifier)])\n",
    "    \n",
    "    pipe = imblearn.pipeline.Pipeline(estimators)\n",
    "    \n",
    "    prequential_split_indices = prequentialSplit(transactions_df,\n",
    "                                                 start_date_training=start_date_training, \n",
    "                                                 n_folds=n_folds, \n",
    "                                                 delta_train=delta_train, \n",
    "                                                 delta_delay=delta_delay, \n",
    "                                                 delta_assessment=delta_assessment)\n",
    "    \n",
    "    grid_search = sklearn.model_selection.GridSearchCV(pipe, parameters, scoring=scoring, cv=prequential_split_indices, refit=False, n_jobs=n_jobs)\n",
    "    \n",
    "    X = transactions_df[input_features]\n",
    "    y = transactions_df[output_feature]\n",
    "\n",
    "    grid_search.fit(X, y)\n",
    "    \n",
    "    performances_df = pd.DataFrame()\n",
    "    \n",
    "    for i in range(len(performance_metrics_list_grid)):\n",
    "        performances_df[performance_metrics_list[i]+' '+expe_type]=grid_search.cv_results_['mean_test_'+performance_metrics_list_grid[i]]\n",
    "        performances_df[performance_metrics_list[i]+' '+expe_type+' Std']=grid_search.cv_results_['std_test_'+performance_metrics_list_grid[i]]\n",
    "\n",
    "    performances_df['Parameters'] = grid_search.cv_results_['params']\n",
    "    performances_df['Execution time'] = grid_search.cv_results_['mean_fit_time']\n",
    "    \n",
    "    return performances_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `model_selection_wrapper` function is also modified into a `model_selection_wrapper_with_sampler` function, which calls the `prequential_grid_search_with_sampler` function for prequential grid search. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def model_selection_wrapper_with_sampler(transactions_df, \n",
    "                                         classifier, \n",
    "                                         sampler_list,\n",
    "                                         input_features, output_feature,\n",
    "                                         parameters, \n",
    "                                         scoring, \n",
    "                                         start_date_training_for_valid,\n",
    "                                         start_date_training_for_test,\n",
    "                                         n_folds=4,\n",
    "                                         delta_train=7, \n",
    "                                         delta_delay=7, \n",
    "                                         delta_assessment=7,\n",
    "                                         performance_metrics_list_grid=['roc_auc'],\n",
    "                                         performance_metrics_list=['AUC ROC'],\n",
    "                                         n_jobs=-1):\n",
    "\n",
    "    # Get performances on the validation set using prequential validation\n",
    "    performances_df_validation = prequential_grid_search_with_sampler(transactions_df, classifier, sampler_list,\n",
    "                            input_features, output_feature,\n",
    "                            parameters, scoring, \n",
    "                            start_date_training=start_date_training_for_valid,\n",
    "                            n_folds=n_folds,\n",
    "                            expe_type='Validation',\n",
    "                            delta_train=delta_train, \n",
    "                            delta_delay=delta_delay, \n",
    "                            delta_assessment=delta_assessment,\n",
    "                            performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                            performance_metrics_list=performance_metrics_list,\n",
    "                            n_jobs=n_jobs)\n",
    "    \n",
    "    # Get performances on the test set using prequential validation\n",
    "    performances_df_test = prequential_grid_search_with_sampler(transactions_df, classifier, sampler_list,\n",
    "                            input_features, output_feature,\n",
    "                            parameters, scoring, \n",
    "                            start_date_training=start_date_training_for_test,\n",
    "                            n_folds=n_folds,\n",
    "                            expe_type='Test',\n",
    "                            delta_train=delta_train, \n",
    "                            delta_delay=delta_delay, \n",
    "                            delta_assessment=delta_assessment,\n",
    "                            performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                            performance_metrics_list=performance_metrics_list,\n",
    "                            n_jobs=n_jobs)\n",
    "    \n",
    "    # Bind the two resulting DataFrames\n",
    "    performances_df_validation.drop(columns=['Parameters','Execution time'], inplace=True)\n",
    "    performances_df = pd.concat([performances_df_test,performances_df_validation],axis=1)\n",
    "\n",
    "    # And return as a single DataFrame\n",
    "    return performances_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model selection with resampling can now be performed by calling the `model_selection_wrapper_with_sampler` function, following the same methodology as in [Chapter 5](Model_Selection). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies_Transaction_Data_Oversampling)=\n",
    "### Oversampling\n",
    "\n",
    "Let us illustrate its use with SMOTE oversampling. We create a `SMOTE` object and store it in a `sampler_list` list. The list is passed as an argument to the `model_selection_wrapper_with_sampler` function. Additionally, the `sampling_strategy` parameter to the `SMOTE` object (imbalance ratio) is parametrized to take values in the set $[0.01, 0.05, 0.1, 0.5, 1]$ for the model selection procedure.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.tree.DecisionTreeClassifier()\n",
    "\n",
    "# Define sampling strategy\n",
    "sampler_list = [('sampler', imblearn.over_sampling.SMOTE(random_state=0))]\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__max_depth':[5], 'clf__random_state':[0],\n",
    "              'sampler__sampling_strategy':[0.01, 0.05, 0.1, 0.5, 1], 'sampler__random_state':[0]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df=model_selection_wrapper_with_sampler(transactions_df, classifier, sampler_list, \n",
    "                                                     input_features, output_feature,\n",
    "                                                     parameters, scoring, \n",
    "                                                     start_date_training_for_valid,\n",
    "                                                     start_date_training_for_test,\n",
    "                                                     n_folds=n_folds,\n",
    "                                                     delta_train=delta_train, \n",
    "                                                     delta_delay=delta_delay, \n",
    "                                                     delta_assessment=delta_assessment,\n",
    "                                                     performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                                     performance_metrics_list=performance_metrics_list,\n",
    "                                                     n_jobs=1)\n",
    "\n",
    "execution_time_dt_SMOTE = time.time()-start_time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then retrieve the performances for each of the tested imbalance ratios (`sampling_strategy` value) and store the results in a `performances_df_SMOTE` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select parameter of interest (sampling_strategy)\n",
    "parameters_dict=dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary']=[parameters_dict[i]['sampler__sampling_strategy'] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_SMOTE for model performance comparison at the end of this notebook\n",
    "performances_df_SMOTE = performances_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.805165</td>\n",
       "      <td>0.005955</td>\n",
       "      <td>0.587136</td>\n",
       "      <td>0.010773</td>\n",
       "      <td>0.282143</td>\n",
       "      <td>0.007389</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.559659</td>\n",
       "      <td>0.803468</td>\n",
       "      <td>0.014269</td>\n",
       "      <td>0.554155</td>\n",
       "      <td>0.032957</td>\n",
       "      <td>0.267500</td>\n",
       "      <td>0.014120</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.823732</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.572835</td>\n",
       "      <td>0.024824</td>\n",
       "      <td>0.285357</td>\n",
       "      <td>0.012753</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.542496</td>\n",
       "      <td>0.809590</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.558479</td>\n",
       "      <td>0.027820</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>0.010785</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.827219</td>\n",
       "      <td>0.010190</td>\n",
       "      <td>0.544275</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>0.290357</td>\n",
       "      <td>0.010070</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.569432</td>\n",
       "      <td>0.807543</td>\n",
       "      <td>0.016324</td>\n",
       "      <td>0.537672</td>\n",
       "      <td>0.040918</td>\n",
       "      <td>0.268214</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.842985</td>\n",
       "      <td>0.018418</td>\n",
       "      <td>0.558118</td>\n",
       "      <td>0.033607</td>\n",
       "      <td>0.289286</td>\n",
       "      <td>0.015860</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.829486</td>\n",
       "      <td>0.849501</td>\n",
       "      <td>0.015830</td>\n",
       "      <td>0.495635</td>\n",
       "      <td>0.052593</td>\n",
       "      <td>0.279286</td>\n",
       "      <td>0.018028</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.845270</td>\n",
       "      <td>0.008136</td>\n",
       "      <td>0.545954</td>\n",
       "      <td>0.031952</td>\n",
       "      <td>0.294643</td>\n",
       "      <td>0.011401</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>1.091997</td>\n",
       "      <td>0.857367</td>\n",
       "      <td>0.012183</td>\n",
       "      <td>0.493752</td>\n",
       "      <td>0.049094</td>\n",
       "      <td>0.278571</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.805165          0.005955                0.587136   \n",
       "1      0.823732          0.006235                0.572835   \n",
       "2      0.827219          0.010190                0.544275   \n",
       "3      0.842985          0.018418                0.558118   \n",
       "4      0.845270          0.008136                0.545954   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.010773                 0.282143   \n",
       "1                    0.024824                 0.285357   \n",
       "2                    0.037686                 0.290357   \n",
       "3                    0.033607                 0.289286   \n",
       "4                    0.031952                 0.294643   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.007389   \n",
       "1                     0.012753   \n",
       "2                     0.010070   \n",
       "3                     0.015860   \n",
       "4                     0.011401   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.559659   \n",
       "1  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.542496   \n",
       "2  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.569432   \n",
       "3  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.829486   \n",
       "4  {'clf__max_depth': 5, 'clf__random_state': 0, ...        1.091997   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.803468                0.014269                      0.554155   \n",
       "1            0.809590                0.018765                      0.558479   \n",
       "2            0.807543                0.016324                      0.537672   \n",
       "3            0.849501                0.015830                      0.495635   \n",
       "4            0.857367                0.012183                      0.493752   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.032957                       0.267500   \n",
       "1                          0.027820                       0.271429   \n",
       "2                          0.040918                       0.268214   \n",
       "3                          0.052593                       0.279286   \n",
       "4                          0.049094                       0.278571   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.014120                0.01  \n",
       "1                           0.010785                0.05  \n",
       "2                           0.013224                0.10  \n",
       "3                           0.018028                0.50  \n",
       "4                           0.014463                1.00  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.857+/-0.01</td>\n",
       "      <td>0.558+/-0.03</td>\n",
       "      <td>0.279+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.845+/-0.01</td>\n",
       "      <td>0.573+/-0.02</td>\n",
       "      <td>0.289+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.845+/-0.01</td>\n",
       "      <td>0.587+/-0.01</td>\n",
       "      <td>0.295+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           1.0              0.05                0.5\n",
       "Validation performance     0.857+/-0.01      0.558+/-0.03       0.279+/-0.02\n",
       "Test performance           0.845+/-0.01      0.573+/-0.02       0.289+/-0.02\n",
       "Optimal parameter(s)                1.0              0.01                1.0\n",
       "Optimal test performance   0.845+/-0.01      0.587+/-0.01       0.295+/-0.01"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_SMOTE = get_summary_performances(performances_df_SMOTE, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note conflicting results, as the optimal imbalance ratio depends on the performance metric. For better visualization, let us plot the performances as a function of the imbalance ratio for the three performance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_SMOTE, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_SMOTE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performances tend to increase with the imbalance ratio for AUC ROC and CP@100. The opposite is however observed for Average Precision. The creation of synthetic samples with SMOTE is therefore beneficial to AUC ROC and CP@100, but detrimental to the Average Precision.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies_Transaction_Data_RUS)=\n",
    "### Undersampling\n",
    "\n",
    "Let us follow the same methodology using random undersampling. The `RandomUnderSampler` object is used, and models are assessed for imbalance ratios taking values in the set $[0.01, 0.05, 0.1, 0.5, 1]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.tree.DecisionTreeClassifier()\n",
    "\n",
    "# Define sampling strategy\n",
    "sampler_list = [('sampler', imblearn.under_sampling.RandomUnderSampler())]\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__max_depth':[5], 'clf__random_state':[0],\n",
    "              'sampler__sampling_strategy':[0.01, 0.05, 0.1, 0.5, 1], 'sampler__random_state':[0]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df=model_selection_wrapper_with_sampler(transactions_df, classifier, sampler_list, \n",
    "                                                     input_features, output_feature,\n",
    "                                                     parameters, scoring, \n",
    "                                                     start_date_training_for_valid,\n",
    "                                                     start_date_training_for_test,\n",
    "                                                     n_folds=n_folds,\n",
    "                                                     delta_train=delta_train, \n",
    "                                                     delta_delay=delta_delay, \n",
    "                                                     delta_assessment=delta_assessment,\n",
    "                                                     performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                                     performance_metrics_list=performance_metrics_list,\n",
    "                                                     n_jobs=1)\n",
    "\n",
    "execution_time_dt_RUS = time.time()-start_time\n",
    "\n",
    "# Select parameter of interest (sampling_strategy)\n",
    "parameters_dict=dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary']=[parameters_dict[i]['sampler__sampling_strategy'] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_RUS for model performance comparison at the end of this notebook\n",
    "performances_df_RUS = performances_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratios, and plot the performances as a function of the imbalance ratio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.845+/-0.01</td>\n",
       "      <td>0.535+/-0.04</td>\n",
       "      <td>0.269+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.83+/-0.01</td>\n",
       "      <td>0.594+/-0.02</td>\n",
       "      <td>0.277+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.83+/-0.01</td>\n",
       "      <td>0.594+/-0.02</td>\n",
       "      <td>0.292+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           1.0              0.01                0.1\n",
       "Validation performance     0.845+/-0.01      0.535+/-0.04       0.269+/-0.01\n",
       "Test performance            0.83+/-0.01      0.594+/-0.02       0.277+/-0.01\n",
       "Optimal parameter(s)                1.0              0.01                0.5\n",
       "Optimal test performance    0.83+/-0.01      0.594+/-0.02       0.292+/-0.01"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_RUS=get_summary_performances(performances_df_RUS, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_RUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_RUS, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_RUS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with oversampling, rebalancing the dataset leads to an increase in performance in terms of AUC ROC, but to a strong decrease of performance in terms of AP. The results in terms of CP@100 follow an intermediate trend: The performance first slightly increases with the imbalance ratio, and then decreases with more aggressive undersampling.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Resampling_Strategies_Transaction_Data_Combining)=\n",
    "### Combining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finally illustrate the combination of oversampling and undersampling with SMOTE and random undersampling. A `SMOTE` and `RandomUnderSampler` objects are instantiated and stored in the `sampler_list` list. We parametrize the `SMOTE` sampler with a target imbalance ratio of $0.1$, and the `RandomUnderSampler` to take values in the set $[0.1, 0.5, 1]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.tree.DecisionTreeClassifier()\n",
    "\n",
    "# Define sampling strategy\n",
    "sampler_list = [('sampler1', imblearn.over_sampling.SMOTE()),\n",
    "                ('sampler2', imblearn.under_sampling.RandomUnderSampler())\n",
    "               ]\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__max_depth':[5], 'clf__random_state':[0],\n",
    "              'sampler1__sampling_strategy':[0.1], \n",
    "              'sampler2__sampling_strategy':[0.1, 0.5, 1], \n",
    "              'sampler1__random_state':[0], 'sampler2__random_state':[0]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df = model_selection_wrapper_with_sampler(transactions_df, classifier, sampler_list, \n",
    "                                                     input_features, output_feature,\n",
    "                                                     parameters, scoring, \n",
    "                                                     start_date_training_for_valid,\n",
    "                                                     start_date_training_for_test,\n",
    "                                                     n_folds=n_folds,\n",
    "                                                     delta_train=delta_train, \n",
    "                                                     delta_delay=delta_delay, \n",
    "                                                     delta_assessment=delta_assessment,\n",
    "                                                     performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                                     performance_metrics_list=performance_metrics_list,\n",
    "                                                     n_jobs=1)\n",
    "\n",
    "execution_time_dt_combined = time.time()-start_time\n",
    "\n",
    "# Select parameter of interest (max_depth)\n",
    "parameters_dict = dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary']=[parameters_dict[i]['sampler2__sampling_strategy'] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_combined for model performance comparison at the end of this notebook\n",
    "performances_df_combined = performances_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.827219</td>\n",
       "      <td>0.010190</td>\n",
       "      <td>0.544275</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>0.290357</td>\n",
       "      <td>0.010070</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.608622</td>\n",
       "      <td>0.807543</td>\n",
       "      <td>0.016324</td>\n",
       "      <td>0.537672</td>\n",
       "      <td>0.040918</td>\n",
       "      <td>0.268214</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.848464</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.471467</td>\n",
       "      <td>0.010144</td>\n",
       "      <td>0.299643</td>\n",
       "      <td>0.011968</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.395396</td>\n",
       "      <td>0.841237</td>\n",
       "      <td>0.015297</td>\n",
       "      <td>0.428576</td>\n",
       "      <td>0.050091</td>\n",
       "      <td>0.279286</td>\n",
       "      <td>0.010903</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.847110</td>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.385133</td>\n",
       "      <td>0.064916</td>\n",
       "      <td>0.287857</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>{'clf__max_depth': 5, 'clf__random_state': 0, ...</td>\n",
       "      <td>0.384152</td>\n",
       "      <td>0.856399</td>\n",
       "      <td>0.012189</td>\n",
       "      <td>0.342964</td>\n",
       "      <td>0.020342</td>\n",
       "      <td>0.280357</td>\n",
       "      <td>0.017420</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.827219          0.010190                0.544275   \n",
       "1      0.848464          0.008701                0.471467   \n",
       "2      0.847110          0.010754                0.385133   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.037686                 0.290357   \n",
       "1                    0.010144                 0.299643   \n",
       "2                    0.064916                 0.287857   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.010070   \n",
       "1                     0.011968   \n",
       "2                     0.018364   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.608622   \n",
       "1  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.395396   \n",
       "2  {'clf__max_depth': 5, 'clf__random_state': 0, ...        0.384152   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.807543                0.016324                      0.537672   \n",
       "1            0.841237                0.015297                      0.428576   \n",
       "2            0.856399                0.012189                      0.342964   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.040918                       0.268214   \n",
       "1                          0.050091                       0.279286   \n",
       "2                          0.020342                       0.280357   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.013224                 0.1  \n",
       "1                           0.010903                 0.5  \n",
       "2                           0.017420                 1.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratios, and plot the performances as a function of the imbalance ratio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.856+/-0.01</td>\n",
       "      <td>0.538+/-0.04</td>\n",
       "      <td>0.28+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.847+/-0.01</td>\n",
       "      <td>0.544+/-0.04</td>\n",
       "      <td>0.288+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.848+/-0.01</td>\n",
       "      <td>0.544+/-0.04</td>\n",
       "      <td>0.3+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           1.0               0.1                1.0\n",
       "Validation performance     0.856+/-0.01      0.538+/-0.04        0.28+/-0.02\n",
       "Test performance           0.847+/-0.01      0.544+/-0.04       0.288+/-0.02\n",
       "Optimal parameter(s)                0.5               0.1                0.5\n",
       "Optimal test performance   0.848+/-0.01      0.544+/-0.04         0.3+/-0.01"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_combined = get_summary_performances(performances_df=performances_df_combined, \n",
    "                                                         parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_combined, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_combined)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results follow the same trends as with oversampling and undersampling: Rebalancing improves the performances in terms of AUC ROC, but decreases the performances in terms of Average Precision. The impact of rebalancing on CP@100 is mitigated.\n",
    "\n",
    "Let us finally compare the test performances obtained with the oversampling, undersampling, and combined resampling, and compare them to the baseline classifier.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline</th>\n",
       "      <th>SMOTE</th>\n",
       "      <th>RUS</th>\n",
       "      <th>Combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUC ROC</th>\n",
       "      <td>0.81+/-0.01</td>\n",
       "      <td>0.845+/-0.01</td>\n",
       "      <td>0.83+/-0.01</td>\n",
       "      <td>0.847+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average precision</th>\n",
       "      <td>0.6+/-0.02</td>\n",
       "      <td>0.573+/-0.02</td>\n",
       "      <td>0.594+/-0.02</td>\n",
       "      <td>0.544+/-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Precision@100</th>\n",
       "      <td>0.284+/-0.0</td>\n",
       "      <td>0.289+/-0.02</td>\n",
       "      <td>0.277+/-0.01</td>\n",
       "      <td>0.288+/-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Baseline         SMOTE           RUS      Combined\n",
       "AUC ROC             0.81+/-0.01  0.845+/-0.01   0.83+/-0.01  0.847+/-0.01\n",
       "Average precision    0.6+/-0.02  0.573+/-0.02  0.594+/-0.02  0.544+/-0.04\n",
       "Card Precision@100  0.284+/-0.0  0.289+/-0.02  0.277+/-0.01  0.288+/-0.02"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_test_performances = pd.concat([summary_performances_dt.iloc[2,:],\n",
    "                                       summary_performances_SMOTE.iloc[2,:],\n",
    "                                       summary_performances_RUS.iloc[2,:],\n",
    "                                       summary_performances_combined.iloc[2,:],\n",
    "                                      ],axis=1)\n",
    "summary_test_performances.columns=['Baseline', 'SMOTE', 'RUS', 'Combined']\n",
    "summary_test_performances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the baseline classifier, all resampling techniques managed to improve the performances in terms of AUC ROC. All of them however led to a decrease in Average Precision. Slight improvements in terms of CP@100 could be achieved with SMOTE and the combined approach, but not with undersampling. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "The experiments carried out in this section illustrated that the benefits of resampling techniques depend on the performance metric that is used. While resampling can generally be beneficial to AUC ROC, we observed that they led in almost all cases to a decrease of performances in terms of Average Precision. It is worth noting that the results are coherent with those obtained using cost-sensitive techniques in [the previous section](Cost_Sensitive_Learning). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving of results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us finally save the performance results and execution times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "performances_df_dictionary={\n",
    "    \"SMOTE\": performances_df_SMOTE,\n",
    "    \"RUS\": performances_df_RUS,\n",
    "    \"Combined\": performances_df_combined\n",
    "}\n",
    "\n",
    "execution_times=[execution_time_dt_SMOTE,\n",
    "                 execution_time_dt_RUS,\n",
    "                 execution_time_dt_combined,\n",
    "                ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "filehandler = open('performances_resampling.pkl', 'wb') \n",
    "pickle.dump((performances_df_dictionary, execution_times), filehandler)\n",
    "filehandler.close()"
   ]
  }
 ],
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